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Full Description
Hyper/multispectral imagery in optical remote sensing is an extension of color RGB pictures. The utilized wavelength range is beyond the visible, up to the reflective shortwave infrared. Hyperspectral imaging offers higher spectral resolution, leading to many wavebands. The spectral profiles recorded reveal reflected solar radiation from Earth-surface materials when the sensor is mounted on an airborne or spaceborne platform. An inverse process using machine-learning approaches is conducted for target detection, material identification, and associated environmental applications, which is the main purpose of remote sensing.
This Field Guide covers three areas: the fundamentals of remote sensing imaging for image understanding; image processing for correction and quality improvement; and image analysis for information extraction at subpixel, pixel, superpixel, and image levels, including feature mining and reduction. Basic concepts and fundamental understanding are emphasized to prepare the reader for exploring advanced methods.
Contents
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
