Hyperspectral Image Analysis

Chapter 1 Introduction

Chapter 2 Machine Learning Methods for Spatial and Temporal Parameter Estimation

Chapter 3 Deep Learning for Hyperspectral Image Analysis, Part I: Theory and Algorithms

Chapter 4 Deep Learning for Hyperspectral Image Analysis, Part II: Applications to Remote Sensing Remote sensing  and Biomedicine

Chapter 5 Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios

Chapter 6 Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis

Chapter 7 Supervised, Semi-supervised, and Unsupervised Learning for Hyperspectral Regression

Chapter 8 Sparsity-Based Methods for Classification

Chapter 9 Multiple Kernel Learning for Hyperspectral Image Classification

Chapter 10 Low Dimensional Manifold Model in Hyperspectral Image Reconstruction

Chapter 11 Deep Sparse Band Selection for Hyperspectral Face Recognition

Chapter 12 Detection of Large-Scale and Anomalous Changes

Chapter 13 Recent Advances in Hyperspectral Unmixing Using Sparse Techniques and Deep Learning

Chapter 14 Hyperspectral–Multispectral Image Fusion Enhancement Based on Deep Learning

Chapter 15 Automatic Target Detection for Sparse Hyperspectral Images