Field Guide to Hyperspectral/Multispectral Image Processing

Erscheinungsjahr 2022
ISBN: 978-1-5106-5214-9
Verlag: SPIE Press

Buch, Englisch, 118 Seiten, Spiral bound

Reihe: Field Guides

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.

Autoren/Hrsg.

Fachgebiete

• Geowissenschaften Geologie Geodäsie, Kartographie, Fernerkundung
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Angewandte Optik
• Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

- 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