Subpixel Mapping for Remote Sensing Images

Description

Subpixel mapping is a technology that generates a fine resolution land cover map from coarse resolution fractional images by predicting the spatial locations of different land cover classes at the subpixel scale. This book provides readers with a complete overview of subpixel image processing methods, basic principles, and different subpixel mapping techniques based on single or multi-shift remote sensing images. Step-by-step procedures, experimental contents, and result analyses are explained clearly at the end of each chapter. Real-life applications are a great resource for understanding how and where to use subpixel mapping when dealing with different remote sensing imaging data. This book will be of interest to undergraduate and graduate students, majoring in remote sensing, surveying, mapping, and signal and information processing in universities and colleges, and it can also be used by professionals and researchers at different levels in related fields. Cover Half Title Title Copyright Contents Foreword Preface Authors Chapter 1 Introduction 1.1 Background and Significance 1.1.1 Background of Subpixel Mapping 1.1.2 Significance of Subpixel Mapping 1.2 Research Status of Subpixel Mapping 1.2.1 Initialize-Then-Optimize Subpixel Mapping 1.2.2 Soft-Then-Hard Subpixel Mapping 1.2.3 Other Types of Subpixel Mapping 1.2.4 Research Status of Super-Resolution Technology 1.3 Problems in Subpixel Mapping 1.4 Main Research Contents and Chapter Arrangement References Chapter 2 Basic Principles of Subpixel Mapping 2.1 Introduction 2.2 Spectral Unmixing Method 2.2.1 Linear Spectral Unmixing Model 2.2.2 Non-linear Spectral Unmixing Model 2.3 Theoretical Basis of Spatial Correlation 2.4 Processing Flow of Subpixel Mapping 2.4.1 Subpixel Sharpening Method 2.4.2 Class Allocation Method 2.5 Evaluation Method of Subpixel Mapping Accuracy 2.6 Summary References Chapter 3 Subpixel Mapping Based on Single Remote Sensing Image 3.1 Introduction 3.2 Subpixel Mapping Based on Spatial-Spectral Interpolation 3.2.1 Interpolation Problem 3.2.2 Existing Subpixel Mapping Based on Interpolation 3.2.3 Processing Flow of the Proposed Method 3.2.4 Experimental Content and Result Analysis 3.3 Subpixel Mapping Based on Hopfield Neural Network With More Supervision Information 3.3.1 Traditional Subpixel Mapping Method Based on Hopfield Neural Network 3.3.2 Hopfield Neural Network With More Prior Information 3.3.3 Experiment Content and Result Analysis 3.4 Subpixel Mapping Based on Extended Random Walk 3.4.1 Multi-Scale Segmentation Algorithm 3.4.2 Extended Random Walk Algorithm 3.4.3 Class Allocation Method Based on Object Unit 3.4.4 Experimental Content and Result Analysis 3.5 Subpixel Mapping Based on Spatial-Spectral Correlation for Spectral Imagery 3.5.1 Spatial Correlation 3.5.2 Spectral Correlation 3.5.3 Spatial-Spectral Correlation Implementation 3.5.4 Experimental Content and Result Analysis 3.6 Summary References Chapter 4 Subpixel Mapping Based on Multi-Shift Remote Sensing Images 4.1 Introduction 4.2 Theoretical Basis 4.2.1 Multi-Shift Images Problem 4.2.2 Existing Subpixel Mapping Method Based on Multi-Shift Images 4.3 Subpixel Mapping Method Based on Multi-Shift With Spatial-Spectral Information 4.3.1 Multi-Shift Image With More Spatial-Spectral Information 4.3.2 Experiment Content and Result Analysis 4.4 Subpixel Mapping Based on the Spatial Attraction Model With Multi-Scale Subpixel Shifted Images 4.4.1 Subpixel-Pixel Spatial Attraction Model 4.4.2 Subpixel-Subpixel Spatial Attraction Model 4.4.3 Spatial Attraction Model With Multi-Scale Subpixel Shifted Image 4.4.4 Experiment Content and Result Analysis 4.5 Utilizing Parallel Networks to Produce Subpixel Shifted Images With Multi-Scale Spatial-Spectral Information for Subpixel Mapping 4.5.1 Multi-Scale Network and Spatial-Spectral Network 4.5.2 Multi-Scale Spatial-Spectral Information 4.5.3 Experimental Content and Result Analysis 4.6 Spatiotemporal Subpixel Mapping by Considering the Point Spread Function Effect 4.6.1 Spatial Dependence 4.6.2 Temporal Dependence 4.6.3 Spatiotemporal Dependence 4.6.4 Experimental Content and Result Analysis 4.7 Summary References Chapter 5 Subpixel Mapping of Remote Sensing Image Based on Fusion Technology 5.1 Introduction 5.2 Soft-Then-Hard Subpixel Mapping Based on Pansharpening Technology 5.2.1 Pansharpening Technology 5.2.2 STHSRM-PAN 5.2.3 Experimental Content and Result Analysis 5.3 Subpixel Land Cover Mapping Based on Parallel Processing Path for Hyperspectral Image 5.3.1 Fusion Path 5.3.2 Deep Learning Path 5.3.3 Dual Processing Path 5.3.4 Experimental Content and Result Analysis 5.4 Subpixel Mapping Based on Multi-Source Remote Sensing Fusion Data for Land Cover Classes 5.4.1 Data-Level Fusion 5.4.2 Feature Fusion 5.4.3 Obtaining Mapping Result 5.4.4 Experimental Content and Result Analysis 5.5 Summary References Chapter 6 Remote Sensing Image Subpixel Mapping Based on Classification Then Reconstruction 6.1 Introduction 6.2 Theoretical Basis 6.2.1 Super-Resolution Algorithm 6.2.2 Fully Supervised Information Classification Algorithm 6.3 Subpixel Mapping Based on MAP Super-Resolution Reconstruction Then Classification 6.3.1 Transformed MAP-Based Super-Resolution Reconstruction 6.3.2 LSSVM Classification Algorithm 6.3.3 Experiment Content and Result Analysis 6.4 Subpixel Mapping Based on Pansharpening Then Classification 6.4.1 Implementation Steps 6.4.2 Experiment Content and Result Analysis 6.5 Summary References Chapter 7 Application of Subpixel Mapping Technology in Remote Sensing Imaging 7.1 Introduction 7.2 Improving Flood Subpixel Mapping for Multispectral Image by Supplying More Spectral Information 7.2.1 Existing SRFIM 7.2.2 SRFIM-MSI 7.2.3 Experiment Content and Result Analysis 7.3 Subpixel Mapping of Urban Buildings Based in Multispectral Image With Spatial-Spectral Information 7.3.1 Spaceborne Multispectral Remote Sensing Image 7.3.2 Experiment Content and Result Analysis 7.4 Multispectral Subpixel Burned-Area Mapping Based on Space-Temperature Information 7.4.1 Space Part 7.4.2 Temperature Part 7.4.3 Implementation of STI 7.4.4 Experiment Content and Result Analysis 7.5 Summary References Appendix: Abbreviations Content Validity Index