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Tabla de Contenidos: “…5 Least-Squares-Based Linear Spectral Unmixing For Pure Endmembers -- 5.1 Linear Spectral Unmixing -- 5.2 Linear Spectral Unmixing of Endmembers: Case Study of Mangrove Ecosystem -- 5.3 Case Study of Fractional Abundance Estimation of Mangrove Species -- 5.3.1 Endmember Detection -- 5.3.2 Fractional Abundance -- 5.3.3 Accuracy Assessment -- 6 Non-Linear Unmixing for Classification of Mixed Endmembers -- 6.1 Limitations of Linear Spectral Unmixing -- 6.2 Non-Linear Unmixing Models -- 6.3 Nascimento's Bilinear Spectral Unmixing Model -- 6.4 Fan's Bilinear Unmixing Model -- 6.5 Hapke's Bidirectional Model -- 6.6 Higher-Order Non-Linear Spectral Unmixing Models -- 6.7 Experimental Results of Analysis of Lower to Higher-Order Non-Linear Models With Case Studies -- 7 Fuzzy Logic-Based Non-Linear Spectral Unmixing -- 7.1 Fuzzy Logic-Based Non-Linear Spectral Unmixing -- 7.2 Fuzzy C-Means (FCM) -- 7.3 Possibilistic C-Means (PCM) -- 7.4 Entropy-Based Fuzzy C-Means Unmixing -- 7.5 Spatial Fuzzy-Based Unmixing -- 7.6 Applications of Fuzzy-Based Non-Linear Unmixing -- 8 Machine Learning Models for Classification of Hyperspectral Data -- 8.1 Machine Learning Models for Classification of Hyperspectral Data -- 8.2 Classification Models -- 8.3 Prediction Models -- 8.3.1 Deep Learning -- 8.4 Merits and Demerits of the Models Through Experimental Analysis -- 8.5 Role of Machine/Deep Learning in Spatial Big Data Analytics -- Case Study: Evaluating the Performance of a Convolutional Neural Network (CNN) Model in Classifying Hyperspectral Images in the Andaman and Nicobar Islands -- 9 Ecodynamic Modeling -- 9.1 Ecodynamic Modeling -- 9.2 The Ecodynamic Model -- 9.2.1 Ecodynamic Model and Time-Series Analyses of Hyperspectral Data -- 9.2.2 Application of the Ecodynamic Model in Change Detection of Coastal Endmembers…”

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