Non-Linear Spectral Unmixing of Hyperspectral Data


Available for Pre-order. Due September 2024.
Non-Linear Spectral Unmixing of Hyperspectral Data Author: Format: Hardback First Published: Published By: Taylor & Francis Ltd
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Pages: 144 Illustrations and other contents: 27 Tables, black and white; 21 Line drawings, black and white; 50 Halftones, black and white; 71 Illustrations, black and white Language: English ISBN: 9781032450490 Category:

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 biome Illustrates modelling the eco-dynamics of Mangrove habitats in the coastal ecosystem Discusses adoption of appropriate technique for handling spatial data (with coarse resolution) Covers machine/deep learning models for classification Implements 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.

Weight0.2847312 kg




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