Field Guide to Hyperspectral/Multispectral Image Processing

Preface
Glossary of Terms and Acronyms
Optical Remote Sensing

Spectral Coverage of Optical Remote Sensing
Spectral Characteristics of Earth Features
Spectral Resolution
Spatial Resolution
Pixel, Subpixel, and Superpixel
Radiometric Resolution
From Raw Data to Information Retrieval
Image Processing Techniques vs Image Types


Image Data Correction

Radiometric Errors Due to Atmosphere
Cloud Removal
Geometric Errors
Mapping Functions and Ground Control Points
Mapping Function Validation
Resampling
Image Registration Example


Image Radiometric Enhancement and Display

Image Histogram
Linear Histogram Modification
Linear Histogram Modification Example
Uniform Histogram and Cumulative Histogram
Histogram Equalization for Contrast Enhancement
Histogram Equalization Example
Color Composite Image Display
Principal Component Transformation for Image Display


Image Geometric Enhancement

Spatial Filtering
Image Smoothing
Speckle Removal
Edge and Discontinuity Detection
Spatial Gradient Detection
Morphological Operations


Hyperspectral Image Data Representation

Image Data Cube Files
Image Space and Spectral Space
Features and Feature Space
Pixel Vector, Image Matrix, and Data Set Tensor
Cluster Space


Image Clustering and Segmentation

Otsu's Method
Clustering Using the Single-Pass Algorithm
Clustering Using the k-Means Algorithm
Clustering Using the k-Means Algorithm Example
Superpixel Generation Using SLIC


Pixel-Level Supervised Classification

Supervised Classification Procedure
Prototype Sample Selection
Training Samples and Testing Samples
Minimum Euclidean Distance Classifier
Spectral Angle Mapper
Spectral Information Divergence
Class Data Modeling with a Gaussian Distribution
Mean Vector and Covariance Matrix Estimation
Gaussian Maximum-Likelihood Classification
Other Distribution Models
Mahalanobis Distance and Classifier
k-Nearest Neighbor Classification
Support Vector Machines
Nonlinear Support Vector Machines


Handling Limited Numbers of Training Samples

Semi-Supervised Classification
Active Learning
Transfer Learning


Feature Reduction

The Need for Feature Reduction
Basic Band Selection
Mutual Information
Band Selection Based on Mutual Information
Band Selection Based on Class Separability
Knowledge-based Feature Extraction
Data-Driven Approach for Feature Extraction
Linear Discriminant Analysis
Orthogonal Subspace Projection
Adaptive Matched Filter
Band Grouping for Feature Extraction
Principal Components Transformation


Incorporation of Spatial Information in Pixel Classification

Spatial Texture Features using GLCM
Examples of Texture Features
Markov Random Field for Contextual Classification
Options for Spectral-Spatial-based Mapping


Subpixel Analysis

Spectral Unmixing
Endmember Extraction
Endmember Extraction with N-FINDR
Limitation of Linear Unmixing
Subpixel Mapping
Subpixel Mapping Example
Super-resolution Reconstruction


Artificial Neural Networks and Deep Learning with CNNs

Artificial Neural Networks: Structure
Artificial Neural Networks: Neurons
Limitation of Artificial Neural Networks
CNN Input Layer and Convolution Layer
CNN Padding and Stride
CNN Pooling Layer
CNN Multilayer and Output Layer
CNN Training
CNN for Multiple-Image Input
CNN for Hyperspectral Pixel Classification
CNN Training for Hyperspectral Pixel Classification


Multitemporal Earth Observation

Satellite Orbit Period
Coverage and Revisit Time
Change Detection


Classification Accuracy Assessment

Error Matrix for One-Class Mapping
Error Matrix for Multiple-Class Mapping
Kappa Coefficient Using the Error Matrix
Model Validation


Bibliography
Index