Chapters authored
Analysis of Coastal Areas Using SAR Images: A Case Study of the Dutch Wadden Sea Region
The increased availability of civil synthetic aperture radar (SAR) satellite images with different resolution allows us to compare the imaging capabilities of these instruments, to assess the quality of the available data and to investigate different areas (e.g., the Wadden Sea region). In our investigation, we propose to explore the content of TerraSAR-X and Sentinel-1A satellite images via a data mining approach in which the main steps are patch tiling, feature extraction, classification, semantic annotation and visual-statistical analytics. Once all the extracted categories are mapped and quantified, then the next step is to interpret them from an environmental point of view. The objective of our study is the application of semi-automated SAR image interpretation. Its novelty is the automated multiclass categorisation of coastal areas. We found out that the north-west of the Netherlands can be interpreted routinely as land surfaces by our satellite image analyses, while for the Wadden Sea, we can discriminate the different water levels and their impact on the visibility of the tidal flats. This necessitates a selection of time series data spanning a full tidal cycle.
Part of the book: Topics in Radar Signal Processing
Artificial Intelligence Data Science Methodology for Earth Observation
This chapter describes a Copernicus Access Platform Intermediate Layers Small-Scale Demonstrator, which is a general platform for the handling, analysis, and interpretation of Earth observation satellite images, mainly exploiting big data of the European Copernicus Programme by artificial intelligence (AI) methods. From 2020, the platform will be applied at a regional and national level to various use cases such as urban expansion, forest health, and natural disasters. Its workflows allow the selection of satellite images from data archives, the extraction of useful information from the metadata, the generation of descriptors for each individual image, the ingestion of image and descriptor data into a common database, the assignment of semantic content labels to image patches, and the possibility to search and to retrieve similar content-related image patches. The main two components, namely, data mining and data fusion, are detailed and validated. The most important contributions of this chapter are the integration of these two components with a Copernicus platform on top of the European DIAS system, for the purpose of large-scale Earth observation image annotation, and the measurement of the clustering and classification performances of various Copernicus Sentinel and third-party mission data. The average classification accuracy is ranging from 80 to 95% depending on the type of images.
Part of the book: Advanced Analytics and Artificial Intelligence Applications
Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures
Deep learning methods are often used for image classification or local object segmentation. The corresponding test and validation data sets are an integral part of the learning process and also of the algorithm performance evaluation. High and particularly very high-resolution Earth observation (EO) applications based on satellite images primarily aim at the semantic labeling of land cover structures or objects as well as of temporal evolution classes. However, one of the main EO objectives is physical parameter retrievals such as temperatures, precipitation, and crop yield predictions. Therefore, we need reliably labeled data sets and tools to train the developed algorithms and to assess the performance of our deep learning paradigms. Generally, imaging sensors generate a visually understandable representation of the observed scene. However, this does not hold for many EO images, where the recorded images only depict a spectral subset of the scattered light field, thus generating an indirect signature of the imaged object. This spots the load of EO image understanding, as a new and particular challenge of Machine Learning (ML) and Artificial Intelligence (AI). This chapter reviews and analyses the new approaches of EO imaging leveraging the recent advances in physical process-based ML and AI methods and signal processing.
Part of the book: Recent Trends in Artificial Neural Networks