Dilution mixing and incubation time.
\r\n\t
",isbn:"978-1-83969-150-8",printIsbn:"978-1-83969-149-2",pdfIsbn:"978-1-83969-151-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"7409b2acd5150a93004300800918b736",bookSignature:"Prof. Karmen Pažek",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10548.jpg",keywords:"Lean Manufacturing, Agriculture, Production and Process, Costs Reduction, Lean Principles, Industry, Tools, Implementation, Sustainability, Modeling, Environment, Planning",numberOfDownloads:11,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 20th 2020",dateEndSecondStepPublish:"November 17th 2020",dateEndThirdStepPublish:"January 16th 2021",dateEndFourthStepPublish:"April 6th 2021",dateEndFifthStepPublish:"June 5th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Pažek is Head of the undergraduate study program Agricultural economics and rural development and Vice-dean for education. She is the author or co-author of 61 scientific papers, 6 scientific books, and 24 book chapters.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"179642",title:"Prof.",name:"Karmen",middleName:null,surname:"Pažek",slug:"karmen-pazek",fullName:"Karmen Pažek",profilePictureURL:"https://mts.intechopen.com/storage/users/179642/images/system/179642.jpg",biography:"Karmen Pažek achieved her Ph.D. at University of Maribor, Faculty of Agriculture in 2006. She is active as Full Professor for Farm management in the Department for Agriculture Economics and Rural Development on Faculty of Agriculture and Life Sciences, University of Maribor. Her research includes development of decision support tools and systems for farm management (simulation modeling, multi-criteria decision analysis, option models, investment analysis) and economics of agricultural production. She is involved in teaching activities as thesis supervisor at postgraduate study programs and involved in national and international research projects. She is author or coauthor of 61 scientific papers (including 34 papers in journals with impact factor), 6 scientific books and 24 book chapters. Currently she is Head of the undergraduate study program Agricultural economics and rural development and Vice dean for education. \r\n\r\nAcademic activities\r\nResearch:\r\n-\tFarm management\r\n-\tDecision support, simulation, forecasting, multi criteria decision making in the area of agriculture with emphasis on field crops, farm tourism and fruit producon\r\n\r\nCurrent Research work:\r\n- Financial parameters assessment based on perfect and in-perfect information in agrifood \r\n systems \r\n- Option modeling of agrifood projects\r\n-\tEfficiency assessment in farm tourism \r\n-\tEfficiency of sugar beet production systems \r\n\r\nTeaching:\r\nUndergraduate Programmes and Courses\r\n-\tFarm management I and II\r\n-\tIntroduction to decision theory\r\n-\tOrganic fam management\r\n-\tManagement od supplementary activities\r\n-\tEconomics and management of rural tourism\r\n-\tSelected issues in agricultural entrepreneurship\r\n\r\nMaster Programmes and Courses\r\n\r\n-\tResearch methods in farm management\r\n-\tDecision theory\r\n-\tProject planning and quality management\r\n-\tOrganic fam management\r\n\r\n \r\nPhD Programme and Course\r\n\r\n-\tProject management (transferable skills)\r\n-\tSelected issues in farm management",institutionString:"University of Maribor",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Maribor",institutionURL:null,country:{name:"Slovenia"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:[{id:"74769",title:"Development of Integrated Lean Six Sigma-Baldrige Framework for Manufacturing Waste Minimization: A Case of NAS Foods Plc",slug:"development-of-integrated-lean-six-sigma-baldrige-framework-for-manufacturing-waste-minimization-a-c",totalDownloads:16,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"247865",firstName:"Jasna",lastName:"Bozic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/247865/images/7225_n.jpg",email:"jasna.b@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"65069",title:"What Is Limulus Amebocyte Lysate (LAL) and Its Applicability in Endotoxin Quantification of Pharma Products",doi:"10.5772/intechopen.81331",slug:"what-is-limulus-amebocyte-lysate-lal-and-its-applicability-in-endotoxin-quantification-of-pharma-pro",body:'Endotoxins, a type of pyrogen, are natural compounds found in the outer cell membrane of Gram-negative bacteria and can impact over 30 biological activities. Endotoxin can lead to cell death by initiating complement activation. The Limulus amebocyte lysate (LAL) test was commercially introduced in the 1970s. LAL is derived from the blood cells, or amebocytes, of the horseshoe crab, Limulus polyphemus. Frederick Bang and Jack Levin observed that blood cells from horseshoe crabs were found to clot in the presence of endotoxin, and this technology was used in the development of endotoxin detection assays. Today, endotoxin tests are performed on raw and in-process materials, and for the final release of products in the pharmaceutical and medical device industries.
Limulus amebocyte lysate test is an aqueous extract of blood cells (amoebocytes) which obtain from the horseshoe crab (Limulus polyphemus). LAL reagent reacts with the bacterial endotoxins or lipopolysaccharide (LPS). LAL test is recommended in all international pharmacopeias as the method for finding bacterial endotoxins. Gram-negative bacteria produce endotoxins (pyrogen). Exceptionally Bacillus thuringiensis, a Gram-positive bacteria produce delta toxin as endotoxins [1] (Figures 1 and 2).
Activation of inflammation in body [1]. Note: LPS, lipoglycan. LAL test used according to the U.S. Food and Drug Administration (FDA) [2] guidelines Substituted for the U.S. Pharmacopeia (USP) pyrogen test (rabbit fever test) European Pharmacopeia (EP) Japanese Pharmacopeia (JP) [3]. LAL is used for human injectable drugs, animal injectable drug, medical devices, raw materials used in production, in process quality control.
Horseshoe crab [1].
Among the most well-known and important applications of the LAL test are the ones related to the pharmaceutical industry. It can be said that the most common pyrogens in pharmaceutical products are endotoxins, which is why the pyrogen tests on rabbits have been replaced by the LAL test according to the recommendations of the international pharmacopeia. One of the reasons that has made the LAL test prevail in the pharmaceutical industry is the careful avoidance by the LAL manufacturers of bringing harm to live animals during both production and testing. It is important to clarify that the crabs, from which part of the hemolymph used for the LAL test was extracted, are returned to alive to their natural habitat with no lasting problems after the extraction.
Gel Clot technique: based on gel formation [4].
Chromogenic method: based on the producing color after cleavage of a synthetic peptide-chromogen complex.
Turbidimetric method: based on forming turbidity after cleavage of an endogenous substrate.
End point method: 0.005 endotoxins units (EU) per ml.
Kinetic method: 0.001 endotoxins units (EU) per ml.
Kinetic method: time taken to reach a specific absorbance at 405 nm (onset time) is determined. The assay requires specialized instrumentation. Take optical density readings at regular intervals. The greatest sensitivity, λ, of lysate is 0.001 EU/ml.
Endpoint chromogenic method [5]: the released amount of pNA can be calculated after a fixed incubation period. A standard curve, consisting of measured optical density plotted against known standard endotoxin concentration. Later used to determine concentrations in the product. The greatest sensitivity, λ, is 0.005 EU/ml UV/visible spectrophotometers.
Add volume of lysate to a volume of product dilution. Incubating the reaction mixture at 37.5°C. Endotoxin in the reaction would activate the LAL reagent. Cleave small chromogenic peptides and liberates pNA. pNA, color is yellow and absorbs light at 405 nm. For samples that absorb at 405–410 nm, Diazo-coupling agent modification may be used. In this method, pNA reacted with nitrite in hydrochloric acid, ammonium sulfamate and N-(1-naphthyl)-ethylenediamine (NEDA). Absorbs at a range between 540 and 550 nm. A standard curve is used to establish concentrations in product specimens.
10 × 75 mm fully depyrogenated borosilicate glass culture tubes (Associates of Cape Cod, Inc. catalog numbers TB050).
Optical reader is capable of reading at 405 nm, or at 540–550 nm for the diazo method. Incubator is able to maintaining 37 ± 1°C. A water bath can be used for the endpoint test tube method. Both devices should have a uniform heat distribution. Test tube racks to hold the tubes and/or incubate dilution and reaction tubes. Micropipettes or disposable pipette tips free of interfering endotoxins and glucans are recommended. Vortex-type mixer, Para film (American National Can™) and hot-air oven with the capacity to heat to at least 250°C for depyrogenation of glassware.
Limulus amebocyte lysate (LAL), LAL reconstitution buffer, control standard endotoxins (CSE), solution 1 (nitrite), solution 1A (0.1 N hydrochloric acid), solution 2 (ammonium sulfamate), solution 3 (N-(1-naphthyl)-ethylenediamine (NEDA)), LRW.
The endotoxins limit for USP/BP sterile WFI is only 0.25 EU/ml; therefore, sterile WFI may contain detectable endotoxins and be unsuitable for use. Use certified LRW to make dilutions of standards, and to prepare positive controls.
Collect aseptically containers that are free of detectable endotoxins in depyrogenated glassware apparatus.
The pH must be 6–8. Adjust the pH of the product specimen with dilute HCl, NaOH, or buffer (free of endotoxins). Dilute concentrated HCl or NaOH with LRW. Use a volume that will not lead to significant dilution of the test specimen. Dilution (LRW) alone can overcome the issue sometimes.
Gently tap the vial of lysate. Loose material fall to the bottom. Break the vacuum by lifting the gray stopper. Do not contaminate the mouth of the vial. Remove and discard the stopper. Start the reconstituted lysate with 3.2 ml buffer. Avoid vigorous mixing that may cause excessive foaming and a loss of sensitivity. Wrap the vials with parafilm and store in a cold place (2–8°C) when not in use and use within 8 h of reconstitution.
This is relatively well stable and, if stored properly, will retain full activity through the expiration date on the label. Store the product at 2–8°C. Excess temperature over 37°C cause rapid deterioration, loss of sensitivity and distinct yellowing.
Each vial of control standard endotoxins (CSE) contains 10 ng of endotoxins. Reconstitute CSE with the volume mentioned on the Certificate of Analysis (CA, which gives the potency of the CSE). Gently knocks the vial of control standard endotoxins (CSE) to cause loose material to fall to the bottom. Break the vacuum by lifting the gray stopper. Do not contaminate the mouth of the vial. Remove the stopper and place it in a cold place aseptically for reuse.
Reconstitute CSE with the volume specified on the Certificate of Analysis (CA, which gives the potency of the CSE) and as directed in the package insert. Place the stopper. Vortex the vial for 40–60 s to form a homogenous mixture. Discard solution if not used immediately, vortex the vial for 30 s prior to use.
Read the tubes UV/visible spectrophotometers (Table 1).
CSE + lysate | Incubation time (min) |
---|---|
50 μl of 0.50 EU/ml + 50 μl | 30 |
50 μl of 0.250 EU/ml + 50 μl | 30 |
50 μl of 0.125 EU/ml + 50 μl | 30 |
50 μl of 0.0625 EU/ml + 50 μl | 30 |
Dilution mixing and incubation time.
Sample + lysate | Incubation (min) |
---|---|
50 μl of sample + 50 μl | 30 |
Stop the reaction by adding 50% acetic acid. Add 0.025 ml (25 μl) read the optical density (OD) at 405 nm read the test.
Reconstitute vial 1 with entire contents of vial, reconstitute vial 2 with 4 ml of water, reconstitute vial 3 with 4 ml of water. Add 0.05 ml (50 μl) of solution 1 (sodium nitrite reconstituted with dilute HCL). Add 0.05 ml (50 μl) of solution 2 (ammonium sulfamate). Add 0.05 ml (50 μl) of solution 3 (NEDA) use new pipette tip agitate the plate to mix. Full color (magenta) should develop immediately. Read the test at 540–550 nm.
Positive control must be included to verify that it is appropriate to use the parameters of a previous (archived) standard curve to calculate endotoxin concentrations.
LRW negative controls should be included with each test
1: Equation of straight line (results)
y = mx + c
m = slop
x = endotoxin concentration,
c = y-intercept and
y = mean absorbance
X = y-c/m
Example calculation
Prepare sample solutions by dissolving or diluting drugs (pH 6.0–8.0). The pH may be adjusted by the use of acid, base, or suitable buffers as recommended. Do not exceed the MVD or MCV while making dilutions and adjusting the pH.
MVD = (endotoxin limit × concentration of sample solution)/(λ)
Endotoxin limit given in USP, concentration of a sample of the label, λ: the labeled lysate sensitivity in the gel-clot technique (IU/ml) or the lowest concentration used in the standard curve for the turbidimetric or chromogenic techniques.
MVC = λ/endotoxin limit
λ: the labeled lysate sensitivity in the gel-clot technique (IU/ml) or the lowest concentration used in the standard curve for the turbidimetric or chromogenic techniques.
Sample 1
Endotoxin limit: 0.5 EU/ml
Concentration of sample: 100 mg/ml
λ: 0.06 EU/ml
MVD = 0.5 EU/ml × 100 mg/ml/0.06 EU/ml
MVD = 833
Add 1 ml of sample 1 in to 832 ml of LRW. Prepare sample 2 in using the same method.
Using 10-fold and 2-fold dilution methods prepare the following dilutions of control standard endotoxins (CSE)
0.5 EU/ml
0.25 EU/ml
0.125 EU/ml
0.0625 EU/ml
Reconstitute the lysate with 3.2 ml of buffer provided with it. Follow the standard procedure for reconstitution.
Stop reaction.
For sample 1 and sample 2:
Stop the reaction by adding 50% acetic acid. Add 0.025 ml (25 μl) (Tables 2 and 3).
CSE + lysate | Incubation (min) |
---|---|
50 μl of 0.50 EU/ml + 50 μl | 30 |
50 μl of 0.250 EU/ml + 50 μl | 30 |
50 μl of 0.125 EU/ml + 50 μl | 30 |
50 μl of 0.0625 EU/ml + 50 μl | 30 |
50 μl of sample 1 + 50 μl | 30 |
50 μl of sample 2 + 50 μl | 30 |
Different dilution of CSE and lysate.
Make two replicates of each CSE and sample preparation to reduce any errors.
Use Microsoft word for further calculations and results. Make standard curve and endotoxin concentration (Figure 3).
Validation of standard curve.
R2 = coefficient of determination
R = correlation coefficient
R ≥ 0.98
R2 = 0.99
R = √R2 = 0.99
Equation of straight line
y = mx + c
m = slop
x = endotoxin concentration
c = y intercept
y = mean absorbance
Equation of straight line
Y = 1.019X − 0.026
X = Y + 0.026/1.019
m = slop = 1.019,
C = y intercept = 0.026,
Y = mean absorbance,
X = endotoxin concentration
X = Y + 0.026/1.019
Y = 0.300, X = 0.300 + 0.026/1.019, X = 0.319EU/ml
X = Y + 0.026/1.019
Y = 0.335, X = 0.335 + 0.026/1.019, X = 0.354 EU/ml
Gel Clot LAL provides a simple positive/negative result and is most often mentioned in pharmacopeial monographs as the official referee test.
This is very easy to perform.
This is not time consuming.
Accuracy is 100 percent.
The LAL Gel-Clot assay, gives a more quantitative measurement of endotoxin over a range of concentrations.
Gel Clot lysate for 20 test, Gel Clot standard 0.5 EU/Vial, LAL reagent water (LRW 50 ml).
Lysate: add 2 mL LRW and mix it slowly. Do not shake and avoid foaming. Transfer 0.1 ml in 20 test tubes. Store it at –degree (in freezer).
Standard: Add 2 mL of LRW in the vial and mix it well for 15 min. Store the vial at 2–8°C. Storage life is 15 days.
Take three test tubes and mark them as test, positive control and negative control [1].
Add your sample in test tube marked as sample. Add standard in test tube marked as Positive control. Add LRW in test tube marked as negative control. Incubate the test tubes at 37 + 2°C for 60 min. After an incubation, check for the gel by inverting the test tube. If the material remains firm in the bottom of the test tube, it means gel has formed. This positive if the material gets the flow down, it means gel has not formed. This means negative.
Take similarly three test tubes as above and add water for injection (WFI) in test tube marked as sample. And proceed as above. The results should be as follows (Table 4):
Sample | Positive control | Negative control | Result |
---|---|---|---|
−ve (gel not formed) | +ve | −ve | Pass |
Sample | Positive control | Negative control | Result |
---|---|---|---|
+ve (gel formed) | +ve | −ve | Fail |
Results shown sample pass or not.
We have to make dilution.
Example: If the product endotoxin limit is 1 EU/ml, then we have to make the dilution as follows:
Since we are using 0.25 EU/ml, this is called lambda. Divide the endotoxin limit of product with lambda
1/0.25 = 1:4
As per USP, we have to test 3 test as follows:
One test tube | 1:3 | The result should be positive |
Second | 1:4 | The result may be positive or negative |
Third | 1:5 | The result should be negative |
This means the product is passed.
Chromogenic lysate [2],
Respective endotoxin standard,
Diazo coupling reagent (set of four bottles).
Note: All reagents must be stored in refrigerator at 2–8°C.
Dissolve 45.6 ml of acetic acid in 1 liter of distilled water. The final concentration of acetic acid is 0.8 M. This solution can be stored for 3 months.
Remove the plastic cover. Wipe off with 70% alcohol around the rubber cap and top portion of every vial. Remove the aluminum cap with sterile and pyrogen free forceps and then cover with depyrogenated aluminum foil to avoid any Endotoxin contamination. (2.8 ml LAL water vial is provided with Endotoxin vial, concentration is mentioned on the label). Pour whole quantity of LAL water into the ET vial and cover with foil. Mix vigorously for at least 10 s by vortexer. During stirring solution must not touch the foil.
Storage: Store reconstituted Endotoxins solution at 4°C in a refrigerator for 14 days. The solution can also be stored at –20°C for a month. Avoid freezing during storage.
Note: Stir every time vigorously before use.
Toxicolor lysate
(Buffer vial 0.35 ml and LAL water are provided with Lysate. Sensitivity is mentioned on the certificate). After taking from the refrigerator, pour whole quantity of buffer and 0.35 ml LAL water into the lysate vial as soon as possible, covers with foil. Then quickly stir to dissolve. Avoid air bubbling during stirring. Place the vial in ice water bath for 2–3 min before use.
Note: Be sure that the reagent is completely dissolved. This reagent must be reconstituted just before use. The reagent is extremely sensitive and must be consumed at one time. Storage should be avoided, but can be stored at −20°C in 0.1 ml dispensed quantities in small test tubes. Use stored lysate if the color is not changed. Reconstituted lysate may only be deep frozen once.
Four bottles are provided with one set, marked as 7, 8, 9 and 10s respectively. Transfer whole quantity of bottle no. 7 s into bottle no. 8 s. Then add 12 ml distilled water into each of bottle no. 9 s and 10s. Ultimately, we will have three bottles 8, 9, and 10 s, which are used stepwise to block the reaction.
The pH of the sample is adjusted by pyrogen free 0.1 N NaOH or 0.1 N HCl. The pH of the sample should be between 6.0 and 8.0.
Arrange test tubes in two stands as under; stand 1—test tubes for sample and standard dilutions; stand 2—test tubes for reaction.
Take 0.05 ml well-mixed sample into small test tubes. If required, make 1/10 dilution of the sample with Pyrogen free water as Below, Take 4.5 ml of pyrogen free water in the test tube. Then add 0.5 ml of well-mixed sample. Vortex mixing for a few seconds.
Take 0.1 ml into a small test tube for further process.
Make a dilution of the endotoxin (concentration 0.470 EU/ml) according to the product limit. For making 0.235 EU/ml (if the product limit is 0.25) proceed as follows;
Take 0.05 ml of the reconstituted endotoxin in the test tube after stirring. Add 0.05 ml of pyrogen free water and vortex to mix. Now the final dilution is 0.235 EU/ml.
Take 0.05 ml of step 2 into a small test tube for further process.
Pour 0.05 ml of pyrogen free water (being used in the test) in small test tube as a blank for further process.
Lysate addition
Place the tube stand for small test tubes (containing the tubes of blank, standard and diluted samples) in ice water bath or suitable ice water container. Add 0.05 ml of lysate to all of the tubes as soon as possible. Stir the contents of every tube soon after the addition of lysate for a few seconds. Avoid foaming.
Soon after the addition of lysate, place the test tube rack in the incubator set at 32.5 + 2.5°C for 30 min. The tube rack can be placed in the water container placed in the incubator.
After completion of the incubation period, place tube rack in ice water bath, then blocks the reaction immediately from one of the two methods mentioned below:
By acetic acid
Add 0.4 ml of 0.8 M acetic acid into each tube and stir to mix.
By diazo coupling reagent
Three bottles of the reagent are used as under;
Add 0.5 ml from bottle no 8 s to each tube and stir to mix.
Add 0.5 ml from bottle no 9 s to each tube and stir to mix.
Add 0.5 ml from bottle no 10s to each tube and stir to mix.
Absorbance reading (using spectrophotometer) measurement at 405 nm
If 0.8 M acetic acid is used to block the reaction, then absorbance reading is taken at 405 nm.
Note: The readings. Glass photocell is used for reading at 405 nm. Because the volume of the tube content is not sufficient, the distilled water is added to each tube and is stirred to mix.
When Diazo coupling reagent is used for blockage of the reaction then the reading is taken at 545 nm. Note all the readings.
Note: Distilled water is used for reference in both cases.
All the absorbance readings are fed in the “Software reader for window version 1.51” to collect the results.
Following Formula is used to calculate the results
Calculations of MVC, MVD and ELC.
Where the lowest sensitivity of lysate, M is the maximum dose/kg body weight and K is constant having value equal to 5.
The author(s) confirm that this chapter content has no conflict of interest.
Typical shortcomings of current image analysis tools are the lack of content understanding. This becomes apparent with current developments in Earth observation and data analysis [1]. In this chapter, we therefore concentrate on artificial intelligence (AI) applications and our solution strategies as our main objectives in the field of remote sensing, i.e., the acquisition and semantic interpretation of instrument data from remote platforms such as aircraft or satellites observing, for instance, atmospheric phenomena on Earth for weather prediction—or icebergs drifting in arctic waters endangering maritime transport. In particular, we will describe the exploitation of imaging data acquired by Earth-observing satellites and their sensors.
\nThese satellites may either circle about the Earth (mostly on low polar Earth orbits) or be operated from stationary or slowly moving points high above our planet (on so-called geostationary or geosynchronous orbits). Typical examples are Earth-observing and meteorological satellites. All these instruments have been designed with dedicated goals that, as a rule, can only be fulfilled by systematic and interactive data processing and data interpretation on the ground. The processing and data analysis chains are then the main candidates where one can and shall apply modern data science approaches (e.g., machine learning and artificial intelligence) in order to fully exploit the full information content of the sensor data.
\nIn general, we have quite a number of different sensors installed on satellites. These include passive instruments observing the backscattered solar illumination or thermal emissions from the Earth—or active imaging instruments (transmitting and receiving light pulses or radio signals toward and from the target area being observed). For the ease of understanding, we will limit ourselves to optical sensors operating in the visible and infrared spectral ranges and to radar sensors applying synthetic-aperture radar (SAR) concepts [2, 3]. These instruments provide large-scale images with a typical spatial resolution of 1–40 m per pixel. The images can be acquired from spacecraft orbits that cover the Earth completely with well-defined repeat cycles.
\nAfter being transmitted to the ground, the image data will have to undergo systematic processing steps. Typically, the processing schemes follow a stepwise approach where for all steps the image data are accompanied by the necessary descriptor data (metadata). The processing chains start with what we call level-0 data consisting of reordered and annotated detector data; level-1 data provide calibrated sensor data, while level-2 data contain data in commonly known physical units preferably on regular spatial or map grids. Then level-3 data are higher-level products such as thematic maps or time series results (obtained by merging or concatenation of several individual images) or similar operations. Finally, users can apply additional interactive processing steps on their own or exploit available software/platform concepts [4].
\nThis principle of ordered value-adding requires well-established techniques for data management, batch processing and databases, local and distributed (cloud) processing, understanding of the information flow, experience with learning principles, knowledge extraction from image and library data, and discovery of image semantics. At present, typical data sources with easy access are publicly available scientific image data provided by the European Copernicus mission with its Sentinel satellites [5, 6] as well as high-resolution remote sensing images [7, 8]. The European Sentinel satellites comprise among others a constellation of SAR imagers (i.e., Sentinel-1A/Sentinel-1B providing typically large radar images, with a ground sampling distance of 20 meters and selectable horizontal and vertical polarizations), and a constellation of optical imagers (i.e., Sentinel-2A/Sentinel-2B delivering typically large multispectral images with 13 different bands and a ground resolution—depending on the bands—of 10–60 m). This space segment of the Copernicus mission is complemented by systematic level-1 and level-2 image data processing on the ground and by support environments that serve as comfortable platforms for further data handling and interpretation covering all aspects of applied data science. These approaches then pave the way for deeper semantic data analysis and understanding as typically required in Earth observation for crop yield predictions, atmospheric research, etc.
\nThe design of Earth observation (EO) missions as constellations of several satellites brings important advantages. However, this is not the case for some of the most popular EO missions. Figure 1 shows typical TerraSAR-X and Copernicus Sentinel overpasses from different orbits and their target areas.
\nSatellite overpasses of Sentinel-1A/Sentinel-1B, Sentinel-2A/Sentinel-2B, and TerraSAR-X (on 23th of August 2018 starting at 14:02 UT) [12].
TerraSAR-X flies on a polar Sun-synchronous circular dawn-dusk orbit. This satellite shares its orbit plane with its twin satellite TanDEM-X (keeping a 97.44° orbital phasing difference) and a repeat cycle of 11 days with 167 orbits per cycle. Due to its flexibility, TerraSAR-X can cover any point on Earth within a maximum of 4.5 days and 90% of the Earth’s surface within 2 days [9].
\nThe Sentinel-1 satellites fly on a near-polar, Sun-synchronous orbit, too. The satellite constellations (comprising Sentinel-1A and Sentinel-1B) share the same orbit plane with a 180° orbital phasing difference and a repeat cycle of 6 days with 175 orbits per cycle. Sentinel-1 can cover the equator on 3 days, the Artic on less than 1 day, and Europe, Canada, and shipping routes in 1–3 days [10].
\nLike the Sentinel-1 constellation, the Sentinel-2 constellations (comprising Sentinel-2A and Sentinel-2B) share the same orbit with a separation of 180°. The repeat cycle is 5 days with 143 orbits per cycle. Sentinel-2 can cover the equator on 5 days under cloud-free conditions and in 2–3 days at mid-latitudes [11].
\nWhen selecting data for fusion, we have to constrain ourselves to data acquired as close as possible in time.
\nThese data handling approaches are typical for recent advances in big data scenarios in distributed systems on the web (e.g., with high data volumes and throughput rates, conventional and innovative data processing steps, additional necessary tools and environments, and greater user expectations). In our case, this affects the tasks of image processing (e.g., data fusion), image understanding, and comparisons with physical models. This can also be seen when we look at the evolution of satellite data analysis. While early concepts started with data being transferred to algorithms, current systems often transfer data to archives, and future systems may support more and more distributed systems.
\nA typical example is the full functionality offered by machine learning tools, while the basic ideas of future data science aspects for Earth observation as seen by the European Space Agency can be found in [13]. In our case, we are interested in applying more theoretical data science, machine learning, and artificial intelligence (for instance, deep learning, powerful classification maps, and prediction results) together with interactive visualization on various information levels. These ideas will be dealt with below for three remote sensing scenarios as detailed in [14]:
Urban monitoring (urban growth and sprawl, urban classification, and semantic indicators)
Quantitative interpretation of forested areas
Disaster monitoring (earthquakes, inundations, mud slides, etc.)
Here traceable products yielding quantitative data about physical phenomena, change maps, and change predictions are among our primary goals. Of course, we have to consider the implementation effort as well as the attainable accuracy of our products. For each scenario dealt with below, the reader should try to understand what the additional value of machine learning, artificial intelligence, and comprehensive use of data science concepts brings about.
\nThe basic terms of machine learning, artificial intelligence, and data science shall be understood in the following sense:
We use the term “machine learning” mainly when we talk about learning target category parameters derived from selected images and applying these parameters to other examples. Currently, we see much progress by “deep” techniques (e.g., deep learning [15, 16]). An important point is the selection of reliable reference data for traceable validation and verification of the methods.
“Artificial intelligence” describes how machine learning results are exploited for further use. Typically this includes recognizing and being aware of typical situations, making decisions based on the recognized high-level parameters, and predicting future developments. To this end, one can profit from external databases complementing machine learning results.
“Data science” covers the entire field of comprehensive data management and tools, machine learning, and artificial intelligence. This includes topics like distributed processing, monitoring of workflows, visualization techniques, and performance monitoring. Even seemingly trivial tasks (e.g., accessing and handling of data) may belong to data science. However, remote sensing still is in urgent need of efficient tools to familiarize the user community with remote sensing opportunities.
When we look at remote sensing in more detail, we currently see many efforts to transform sensor data to physical quantities that can be exploited for quantitative analysis or modeling. If we accomplish this, we can combine measured data with physical models and find quantitative parameters for predictions.
\nIn the following, we describe how we applied these concepts in a research project funded by the European Union [17]; the project’s main objective is to allow the creation of added value from Copernicus data through the provisioning of modeling and analytics tools for data collection, processing, storage, and access that are provided by the Copernicus Data and Information Access Services (DIAS) [18] and creating a data science workflow where sub-images (image chips) are annotated, administered, and validated based on their assigned semantic labels [19].
\nThe chapter is organized in seven main sections. Section 2 explains the CANDELA platform used for prototyping EO applications, while Section 3 describes the characteristics of the data set. Section 4 presents typical examples which a user can obtain when using the platform from Section 2 and the data set from Section 3. Section 5 illustrates the perspectives in EO data science workflows and Section 6 summarizes our conclusions, while Section 7 contains the future work. The chapter ends with acknowledgments and a list of references.
\nCANDELA’s main objective is the creation of additional value from Copernicus data through the provisioning of modeling and analytics tools provided that the tasks of data collection, processing, storage, and access will be carried out by the Copernicus Data Information and Access Service [18]. The corresponding flowchart is presented in Figure 2 and in [17]. In the end, after the integration of all components, CANDELA will be deployed on top of DIAS.
\nCANDELA platform [17].
The CANDELA platform [17] allows prototyping of EO applications by applying efficient data retrieval, data mining augmented with machine learning techniques, as well as interoperability in order to fully benefit from the available assets and to add more value to the satellite data. It also helps to interactively detect objects or structures and to classify land cover categories.
\nThe implementation of the platform is putting in place a set of powerful tools in artificial intelligence environments (e.g., with machine learning and deep learning). These tools have as their objectives:
To process large volumes of EO data and to perform data analytics
To extract the information content from the EO data based on data mining
To fuse various EO sensors in order to increase and to complement the information extracted from different sensors
To apply deep learning to detect changes in EO data
To semantically search and index our EO image catalog
From this list of objectives, we focus on two of them, namely, data mining and data fusion (see Figure 3). Our goal is to simplify data access and to analyze large volumes of EO data without specific knowledge about the processing of EO data and to fuse the outputs for content exploration.
\nBlock diagram of the CANDELA platform modules [17].
For the development of the data mining component, we started from [20], and we improved the cascaded active learning system of [21] for typical Copernicus Earth observation images. Its implementation, test, and validation aim at automated knowledge extraction and image content interpretation. The results are presented in Section 4.1.
\nRegarding data fusion, a new sub-component had to be developed within data mining. This new sub-component fuses multispectral and SAR images. There are two types of fusions; one is performed at the feature level and the other one at the semantic level. The results are shown in Section 4.2 for feature-level fusion.
\nOur main data sets extracted from different instruments are Earth’s surface images of the European Copernicus Programme (e.g., Sentinel-1 and Sentinel-2). Sentinel-1 is a twin satellite synthetic-aperture radar configuration, while Sentinel-2 is also a twin satellite configuration, each carrying a multispectral imager [22, 23].
\nThere are three reasons why we are selecting and using Sentinel-1 and Sentinel-2 images. Firstly, we can recognize different target area details in overlapping radar and optical images complementing each other with rapid succession. Secondly, individually selectable Sentinel-1 and Sentinel-2 images can be rectified and co-aligned by publicly available toolbox routines offered by ESA allowing a straightforward image comparison or image fusion. Thirdly, all Sentinel instruments are totally openly available to the EO community. Many publications (dedicated conferences [1, 24, 25, 26]) already describe newly discovered Earth’s surface characteristics derived from the individual instruments.
\nFurthermore, the long-term operations of the Sentinel satellites allow the interpretation of image time series or even the combination of time series data with external supplementary data via additional data mining and data fusion tools [1, 25, 26].
\nBesides these data sets, we include other third-party EO mission data sets as specified by CANDELA users (e.g., TerraSAR-X and WorldView).
\nThe Sentinel-1 mission comprises a constellation of two satellites (launched on April 1, 2014, and on April 25, 2016), operating in C-band for synthetic-aperture radar imaging. SAR has the advantage of operating at wavelengths not impeded by thin cloud cover, or a lack of solar illumination, and can acquire data over a selected area during day- or nighttime under nearly no weather condition restrictions. The repeat period of each satellite is 12 days; that means every 6 days there is an acquisition by one of the two satellites.
\nThe Sentinel-1 characteristics are presented in detail in [22]. From the multitude of parameters/configurations that exist for Sentinel-1, we have selected as examples the following configurations based on data availability, the CANDELA use cases, and our previous experiments: level-1 Ground Range Detected (GRD) products with high resolution (HR) taken routinely in Interferometric Wide (IW) swath mode. These products/data are produced (prior to geo-coding) with a pixel spacing of 10 × 10 m and correspond to about five looks and a resolution (range × azimuth) of 20 × 22 m. They have a nearly uniform signal-to-noise ratio (SNR) and also a stable distributed target ambiguity ratio (DTAR). For these products, the data are provided in dual polarization, VV and VH for land and HH and HV for polar target areas.
\nThe Sentinel-2 mission (like Sentinel-1) comprises a constellation of two satellites (launched on June 23, 2015, and on March 7, 2017) able to collect multispectral data and is affected by the weather conditions (e.g., cloud cover). The repeat period of each satellite is 10 days; that means every 5 days there is an acquisition of one of the two satellites, thus providing a high revisit frequency.
\nEach Sentinel-2 satellite carries a multispectral instrument with 13 spectral channels (in the visible/near-infrared and shortwave infrared spectral range) and with 290 km swath width. The Sentinel-2 characteristics are presented in detail in [23]. This also applies to level-1 data; level-1C of these products are radiometrically and geometrically corrected images with orthorectification and spatial registration on a global reference system with sub-pixel accuracy. Since the product size is very large, each image is divided into several quadrants in UTM WGS84 projection. The average size of a quadrant is 10,980 × 10,980 pixels (rows × columns). For visualization, the RGB bands (B04, B03, and B02) were used to generate a quick-look quadrant image. For feature extraction, the user can choose different band combinations.
\nFrom the available third-party mission data sets, we selected for demonstration four pairs of multi-sensor images of TerraSAR-X and WorldView-2 [27].
\nTerraSAR-X is a German radar satellite launched in June 2007, followed by its TanDEM-X twin in 2010. Both operate in X-band and are side-looking SAR instruments that offer a wide selection of operating modes and product generation options [7]. TerraSAR-X has a revisit cycle of 11 days on the Earth’s equator. We selected high-resolution spotlight mode images because they provide the highest-resolution data of the target areas. As for the product generation options, we took enhanced ellipsoid corrected (EEC) and radiometrically enhanced (RE) data. Finally, we took horizontally polarized (HH) or vertically polarized (VV) images, as this option is most frequently used. The images have a pixel spacing of 1.25 m and a resolution of 2.9 m with WGS-84 map projection. The average size of the images is 8000 rows × 9600 columns.
\nIn contrast, WorldView-2 provides a single panchromatic band and eight multispectral bands. It was launched in October 2009 to become a DigitalGlobe satellite. The revisit period of the satellite is about 3 days on the Earth’s equator [28]. The resolution for the panchromatic band is 0.46 m and for multispectral bands is 1.87 m. The map projection of WorldView-2 is, again, WGS-84, and the size of these images (on average) for panchromatic images is 47,000 × 37,000 pixels (rows × columns) and for multispectral images is 11,000 × 9000 pixels (rows × columns).
\nIn EO data mining, a number of researchers have already developed technologies for semantic image understanding [29, 30]. The available web engines are focused on the everyday needs of a broad category of users [31]. A very popular satellite image data mining system is Tomnod from DigitalGlobe or Google Earth, which is targeting general user topics. Especially for EO, there are systems such as LandEX [32] which is a land cover management system, while GeoIRIS [33] is a system that allows the user to refine a given query by iteratively specifying a set of relevant and a set of nonrelevant images. A similar system is IKONA [34] which is using relevance feedback in order to analyze the content of very high-resolution EO images. Further, the knowledge-driven information mining (KIM) system [41] is an example of an active learning system providing semantic interpretation of image content. The KIM concept evolved into the TELEIOS prototype [36], complementing the scope of searching EO images with additional geo-information and in situ data. Finally, a cascaded active learning prototype [21] has been integrated into an operational EO system [20] to interpret the archives of TerraSAR-X images [37].
\nCANDELA is improving this cascaded active learning system by searching for dedicated algorithms for typical Earth observation images. Its implementation, test, and validation aim at automated knowledge extraction and image content interpretation. The targeted performance characteristics are verified for several typical use cases and tell us more about the potential of dedicated algorithms with respect to general machine learning.
\nFigures 4–9 depict typical classification maps for TerraSAR-X and Sentinel-1 images together with their respective accuracy (e.g., precision/recall) for the cities of Venice, Italy, and Munich, Germany. Another example is the Dutch part of the Wadden Sea in the Netherlands. The results of the classification map and their accuracy are given in Figures 10 and 11.
\nTerraSAR-X image of Venice, Italy: (left) a quick-look view of the image and (right) the corresponding classification map generated by CANDELA.
Sentinel-1 image of Venice, Italy (after selecting the area that is covered by TerraSAR-X from the full Sentinel-1 image): (bottom-left) a quick-look view of the image and (bottom-right) the classification map generated by CANDELA.
Classification accuracy (precision/recall) by comparison between TerraSAR-X (top-left) and Sentinel-1 (bottom-right) for the Venice image.
TerraSAR-X image of Munich, Germany: (left) a quick-look view of the image and (right) the classification map generated by CANDELA.
Sentinel-1 image of Munich, Germany (after selecting the area that is also covered by TerraSAR-X): (bottom-left) a quick-look view of the image and (bottom-right) the classification map generated by CANDELA.
Classification accuracy (precision/recall) by comparison between TerraSAR-X (top-right) and Sentinel-1 (bottom-left) for the Munich image.
Sentinel-2 quadrant image of an area of the Dutch Wadden Sea: (left) a quick-look view of the image and (right) the classification map generated by CANDELA.
Classification accuracy (precision/recall) for the Sentinel-2 quadrant image covering an area of the Wadden Sea.
Currently, what exists in the field of data fusion is a collection of routines/algorithms that can be linked and embedded for various applications. A very well-known open-source toolbox is Orfeo [38] which provides a large number of state-of-the-art algorithms to process SAR and multispectral images for different applications. Another one is Google Earth [31] that includes a large image database and an expandable number of algorithms that can be used for image processing.
\nIn our case, we need to recognize different target area details in overlapping SAR and multispectral images. For doing this, we selected a number of cities from all over the world. The cities are Bucharest in Romania, Munich in Germany, Venice in Italy, and Washington in the USA. The selection criteria of these cities were the simultaneous availability of these cities covered by the two satellites and the variety of categories that can be found. A difficulty arises when trying to co-align these images, for example, images provided by TerraSAR-X and WorldView-2, because the original data have different pixel spacing. To solve this problem, we resampled the panchromatic WorldView-2 image in order to co-align it with the TerraSAR-X image [27].
\nIn the case of Sentinel-1 and Sentinel-2, the images can be rectified and co-aligned by publicly available toolbox routines [39]; this allowed us a straightforward image comparison.
\nWhile we are accustomed to image fusion as a radiometric combination of multispectral images, a comparably mature level of semantic fusion of SAR images has not been reached yet. In order to remedy the situation, we propose a semantic fusion concept for SAR images, where we combine the semantic image content of two data sets with different characteristics. By exploiting the specific imaging details and the retrievable semantic categories of the two image types, we obtained semantically fused image classification maps that allow us to differentiate between different categories.
\nFigures 12–14 present the classification maps for each sensor and the fused ones together with their accuracy (e.g., precision/recall) for the city of Venice, while Figures 15–17 apply to the city of Munich.
\nA multi-sensor data set: multispectral image (top-left side), panchromatic image (top-right side), and TerraSAR-X image (bottom-center) for the city of Venice, Italy.
Classification maps generated using the CANDELA platform for the city of Venice: multispectral image (top-left side), panchromatic image (top-right side), TerraSAR-X image (bottom-left side), and fusion of all three images (bottom-right side).
Classification accuracy (precision/recall) for a selected image taken over the area of Venice using multispectral, panchromatic, and SAR images and also the fused image.
A multi-sensor data set: multispectral image (top-left side), panchromatic image (top-right side), and TerraSAR-X image (bottom-center) for the city of Munich, Germany.
Classification maps generated using the CANDELA platform for the city of Munich: multispectral image (top-left side), panchromatic image (top-right side), TerraSAR-X image (bottom-left side), and fusion of all three images (bottom-right side).
Classification accuracy (precision/recall) for a selected image over the area of Munich using multispectral, panchromatic, and SAR images and also the fused image.
For a quantitative assessment, we compared the semantic annotation results with the given reference data set and computed precision/recall for each category and sensor. Analyzing the figures separately, we observed that the average of precision/recall obtained for fused sensor images is higher than the precision/recall of individual sensor images. Unfortunately, there are also cases in which for corresponding image patches tiled from different sensor images, the WorldView-2 annotations have a different semantic classification when compared to the TerraSAR-X results or when a category is missing for one sensor. In our case, in the Venice image, the category “buoys” is only detected in the TerraSAR-X image, and not in the WorldView-2 image. This has a noticeable impact on the performance of the category “boats.” Another example is the category “clouds” that appears in the case of the Munich image that is detected in the WorldView-2 image, but not in the TerraSAR-X image.
\nRecently, a new paradigm for Earth observation, namely, Data Knowledge Discovery, was introduced [17]. This paradigm defines the entire chain “data-information-knowledge-value” and deals with a meaningful EO content extraction, i.e., the semantic and knowledge aspects.
\nWe developed user-invariant and EO domain-specific compensatory methods for the individual user- and domain-subjective biases. The derived models generate a sharable knowledge body as a means to enable the communication between fragmented knowledge learned from metadata, image data, and other data in synergy with the domain expertise of EO users. Today’s EO paradigms and technologies are largely domain-oriented and have to support the communication outlined above.
\nArtificial intelligence big data in Earth observation [13] forced the development of new technologies starting from management platforms [4] and is reaching now the information platforms.
\nAn example for the first category are ESA’s Thematic Exploitation Platforms (TEPs) [4] that are designed and focused for coastal applications, forest, geohazards, hydrology, polar, urban, and food and security application domains, integrating standard processing chains that have low user interaction. The Copernicus system (currently still under development) and its data information and access services component [18] are a major achievement but still represent a “classic” management paradigm.
\nCurrently, “classic” existing systems/platforms are usually batch-oriented (e.g., TEPs, DIAS), but with EOLib [20, 40] and the new CANDELA platform [17], this paradigm was “moved” to interactive systems (e.g., supporting active learning).
\nThere are three perspectives to describe this type of interactive systems:
The first one is based on signal-information logic (Figures 18 and 19).
The objective is the knowledge extraction from the sensor signal of the physically meaningful parameters or Earth’s surface cover categories.
The process is divided in two steps:
The first step is an automated batch process to manage the satellite image product files, i.e., to extract the image data and to select the relevant metadata, to perform a spatial breakdown of the image into patches, to estimate for each image patch the particular signatures or primitive descriptors, and to further structure the extracted information in a database.
In a second step following interactive machine learning paradigms, the extracted information is transformed into semantic entities attached to each image location. The process is a combination of querying, browsing, and active learning. Using positive examples, i.e., training samples for the categories of interest and complemented by negative examples to enhance the accuracies of each class, a user can define the image semantics adapted to a particular application.
The second perspective is based on the value-adding logic (Figure 20).
Based on these procedures, value-adding is an iterative process.
The satellite data are generally multi-mission data, e.g., multispectral and SAR data that are restructured in a common database, which becomes the data source. The data preparation component is generating the Analysis-Ready Data (ARD) ensuring the least and mandatory processing and organizational steps that enable a direct analysis, thus minimizing the user interaction at the data level.
The signal-information logic scheme: chain data-information-knowledge.
The signal-information logic scheme: chain data-information-knowledge-semantic value.
The value-adding logic scheme.
Among them are the generation of radiometrically and geometrically calibrated data cubes. Browsing the data sets is a first step of visual inspection where the user is getting acquainted with the observed structures and their signatures. Further, data mining is an automated process to discover the main data particularities and categories but also detect artifacts or outliers in the data sets, which are beyond the capabilities of human observation, due to the large data volumes and the nonvisual nature of the satellite images. The discovered and selected data sets are further analyzed in detail by extracting the particular characteristics of the observed scenes or objects. The results of the analysis are contributing to update existing models or build new models for the observations. Visualization of the model parameters or extracted information is a verification step to cope with large complex data volumes. Specific evaluation paradigms are needed to build trust in the obtained results, to be used to make predictions. The process is iterative, and when new data are acquired, they will be analyzed further.
The third perspective is the implementation architecture logic (Figure 21).
The implementation of these paradigms requires a concept of integrating artificial intelligence with software (SW) system architectures enabling interactive multiuser operations in real time relative to the user reaction times. End users will be able to work on shared user scenarios, results of their analyses, or information extraction procedures.
The central component is a data index (DI) which is a very specific database model for very fast, real-time management, processing, and distribution of large structured and unstructured distributed multi-temporal data sets. The data can be efficiently uploaded on demand, coping with large volumes of data from various heterogeneous sources.
The data preparation needs to be able to support various tasks for the ARD generation. A workflow orchestration engine will be relaying data and offers various processor steps:
A deep neural network (DNN module) for physically meaningful feature learning
Spatiotemporal analysis, e.g., spatiotemporal pattern analysis and extraction for understanding the evolution classes, fusing information from various sources, not just identifying objects, but in particular spatiotemporal patterns and context
Data mining to explore heterogeneous multi-temporal data sets.
The logic implementation architecture scheme.
The extracted information and data content are again indexed in the DI and provided (via web services) to one of the four human-machine interface (HMI) modules (i.e., visual browsing, visual analytics, active learning, and event analysis) supporting advanced big data visualization and active learning paradigms. Once a researcher is satisfied with the results, they can be shared with a restricted group or publically via the collaborative layer. These architectures are generically based on federated approaches, making it possible to deploy various components where they fit best, using cloud technologies and web services for communication.
\nThe advantages and benefits of the proposed approach are:
We do clustering considering the physical parameters behind the sensors contrary with the classical classification proposed in AI.
With very few examples, we are able to classify the images with high accuracy.
We are able to process multi-sensor data.
We are able to create a semantic scheme adapted to different EO sensors (SAR or multispectral), high resolution (e.g., TerraSAR-X or WorldView)/medium resolution (e.g., Sentinel-1 or Sentinel-2).
During the next years, we expect a wide variety of new satellite image data that can be easily downloaded, handled, and analyzed by individual users. We also think that a number of new geophysical databases and browse tools will become available so that each user has easy access to numerous additional satellite data sources together with auxiliary geophysical data from common libraries and data management tools supporting in-depth image data analyses and their interpretation. Innovative application fields (such as autonomous driving based on machine learning and artificial intelligence) will bring us still more data handling tools and new data archives becoming available via the Internet. In addition, we also suppose that these new tools will be supplemented by management and support environments, for instance, for system testing and performance monitoring. Within the next 5 years, this should result in new established environments for image data understanding.
\nPart of this work was supported by CANDELA—the Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator—a H2020 research and innovation project under grant agreement no. 776193.
\nAnother part of the work was supported by EOLib—the Earth Observation Image Librarian—an ESA technological project.
\nThe TerraSAR-X image data being used in this study were provided by the TerraSAR-X Science Service System (Proposal MTH 1118), while the WorldView-2 image data were provided by the European Space Imaging (EUSI).
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