Karvhari V. Kale

By Karbhari V. Kale, Mahesh M. Solankar and Dhananjay B. Nalawade

Hyperspectral data processing and analysis mainly plays a vital role in detection, identification, discrimination and estimation of earth surface materials. It involves atmospheric correction, dimensionality reduction, endmember extraction, spectral unmixing and classification phases. One of the ultimate aims of hyperspectral data processing and analysis is to achieve high classification accuracy. The classification accuracy of hyperspectral data most probably depends upon image-derived endmembers. Ideally, an endmember is defined as a spectrally unique, idealized and pure signature of a surface material. Extraction of consistent and desired endmember is one of the important criteria to achieve the high accuracy of hyperspectral data classification and spectral unmixing. Several methods, strategies and algorithms are proposed by various researchers to extract the endmembers from hyperspectral imagery. Most of these techniques and algorithms are significantly dependent on user-defined input parameters, and this issue is subjective because there is no standard specificity about these input parameters. This leads to inconsistencies in overall endmember extraction. To resolve the aforementioned problems, systematic, generic, robust and automated mechanism of endmember extraction is required. This chapter gives and highlights the generic approach of endmember extraction with popular algorithm limitations and challenges.

Part of the book: Processing and Analysis of Hyperspectral Data

By Siddharth B. Dabhade, Nagsen S. Bansod, Yogesh S. Rode, Narayan P. Bhosale, Prapti D. Deshmukh and Karbhari V. Kale

Biometrics is mostly used for authentication purposes in security. Due to the covid-19 pandemic situation, nowadays distance-based authentication systems are more focused. Face recognition is one of the best approaches which can use for authentication at distance. Face recognition is a challenging task in various environments. For that taking input from the camera is very important for real-time applications. In this chapter, we are more focusing on how to acquire the face image using MATLAB. The complete chapter is divided into five sections introduction, definition of biometrics, image acquisition devices, image acquisition process in MATLAB.

Part of the book: Recent Advances in Biometrics