Non–Linear Spectral Unmixing of Hyperspectral Data

This book is based on satellite image processing focussing on the potential of hyperspectral image processing (HIP) research taking a case study-based approach. It covers the background, objectives, and practical issues related to HIP and substantiates the needs/potentials of said technology for discrimination of pure and mixed endmembers in pixels including unsupervised target detection algorithms for extraction of unknown spectra of pure pixels. It includes application of machine and deep learning models on hyperspectral data and its role in Spatial Big Data Analytics.
Features:
Focusses on capability of Hyperspectral data in characterization of linear and non-linear interactions of a natural forest biomeIllustrates modelling the eco-dynamics of Mangrove habitats in the coastal ecosystemDiscusses adoption of appropriate technique for handling spatial data (with coarse resolution)Covers machine/deep learning models for classificationImplements non-linear spectral unmixing for identifying fractional abundance of diverse mangrove species of Coastal Sunderbans
This book is aimed at researchers and graduate students in digital image processing, big data, and spatial informatics.


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