Share Email Print

Spie Press Book • new

Field Guide to Hyperspectral / Multispectral Image Processing
Author(s): Xiuping Jia
Format Member Price Non-Member Price

Book Description

Hyper/multispectral imagery in optical remote sensing utilizes wavelengths that range from the visible to the reflective shortwave infrared. Inverse processes using machine learning are applied to the spectral profiles recorded for target detection, material identification, and associated environmental applications, which is the main purpose of remote sensing. This Field Guide covers the fundamentals of remote sensing spectral 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 feature reduction. Basic concepts and fundamental understanding are emphasized to prepare the reader for exploring advanced methods.
;

Book Details

Date Published: 1 July 2022
Pages: 118
ISBN: 9781510652149
Volume: FG52

Table of Contents
SHOW Table of Contents | HIDE Table of 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

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.

I owe thanks to Professor John Richards, who introduced me to the remote sensing field and supervised my Ph.D. study on hyperspectral image classification. I learned a lot from John about critical thinking and thorough presentation.

I dedicate this Field Guide to my husband, Xichuan, my son, James, and my daughter, Jessica, who have shared and accompanied me on my learning journey in this subject for so many years and make me a proud career wife and mom.

Xiuping Jia
The University of New South Wales,
Canberra, Australia
May 2022


© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray