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Subpixel Mapping for Remote Sensing Images [Kõva köide]

, (National Research Council, Canada)
  • Formaat: Hardback, 268 pages, kõrgus x laius: 234x156 mm, kaal: 449 g, 41 Tables, black and white; 52 Line drawings, color; 16 Line drawings, black and white; 105 Halftones, color; 6 Halftones, black and white; 157 Illustrations, color; 22 Illustrations, black and white
  • Ilmumisaeg: 15-Dec-2022
  • Kirjastus: CRC Press
  • ISBN-10: 1032229381
  • ISBN-13: 9781032229386
Teised raamatud teemal:
Subpixel Mapping for Remote Sensing ImagesSuurem pilt
  • Formaat: Hardback, 268 pages, kõrgus x laius: 234x156 mm, kaal: 449 g, 41 Tables, black and white; 52 Line drawings, color; 16 Line drawings, black and white; 105 Halftones, color; 6 Halftones, black and white; 157 Illustrations, color; 22 Illustrations, black and white
  • Ilmumisaeg: 15-Dec-2022
  • Kirjastus: CRC Press
  • ISBN-10: 1032229381
  • ISBN-13: 9781032229386
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032229386.html
Märksõnad:
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.
Foreword ix
Preface xi
Authors xiii
Chapter 1 Introduction
1(22)
1.1 Background and Significance
1(5)
1.1.1 Background of Subpixel Mapping
1(3)
1.1.2 Significance of Subpixel Mapping
4(2)
1.2 Research Status of Subpixel Mapping
6(6)
1.2.1 Initialize-Then-Optimize Subpixel Mapping
7(1)
1.2.2 Soft-Then-Hard Subpixel Mapping
8(1)
1.2.3 Other Types of Subpixel Mapping
9(2)
1.2.4 Research Status of Super-Resolution Technology
11(1)
1.3 Problems in Subpixel Mapping
12(1)
1.4 Main Research Contents and
Chapter Arrangement
13(10)
References
15(8)
Chapter 2 Basic Principles of Subpixel Mapping
23(12)
2.1 Introduction
23(1)
2.2 Spectral Unmixing Method
23(1)
2.2.1 Linear Spectral Unmixing Model
23(1)
2.2.2 Non-linear Spectral Unmixing Model
24(1)
2.3 Theoretical Basis of Spatial Correlation
24(1)
2.4 Processing Flow of Subpixel Mapping
25(5)
2.4.1 Subpixel Sharpening Method
25(3)
2.4.2 Class Allocation Method
28(2)
2.5 Evaluation Method of Subpixel Mapping Accuracy
30(3)
2.6 Summary
33(2)
References
34(1)
Chapter 3 Subpixel Mapping Based on Single Remote Sensing Image
35(50)
3.1 Introduction
35(1)
3.2 Subpixel Mapping Based on Spatial-Spectral Interpolation
35(13)
3.2.1 Interpolation Problem
36(1)
3.2.2 Existing Subpixel Mapping Based on Interpolation
37(1)
3.2.3 Processing Flow of the Proposed Method
38(2)
3.2.4 Experimental Content and Result Analysis
40(8)
3.3 Subpixel Mapping Based on Hopfield Neural Network With More Supervision Information
48(7)
3.3.1 Traditional Subpixel Mapping Method Based on Hopfield Neural Network
48(1)
3.3.2 Hopfield Neural Network With More Prior Information
49(2)
3.3.3 Experiment Content and Result Analysis
51(4)
3.4 Subpixel Mapping Based on Extended Random Walk
55(9)
3.4.1 Multi-Scale Segmentation Algorithm
55(2)
3.4.2 Extended Random Walk Algorithm
57(1)
3.4.3 Class Allocation Method Based on Object Unit
58(1)
3.4.4 Experimental Content and Result Analysis
59(5)
3.5 Subpixel Mapping Based on Spatial-Spectral Correlation for Spectral Imagery
64(17)
3.5.1 Spatial Correlation
64(2)
3.5.2 Spectral Correlation
66(1)
3.5.3 Spatial-Spectral Correlation Implementation
67(2)
3.5.4 Experimental Content and Result Analysis
69(12)
3.6 Summary
81(4)
References
82(3)
Chapter 4 Subpixel Mapping Based on Multi-Shift Remote Sensing Images
85(60)
4.1 Introduction
85(1)
4.2 Theoretical Basis
85(3)
4.2.1 Multi-Shift Images Problem
85(2)
4.2.2 Existing Subpixel Mapping Method Based on Multi-Shift Images
87(1)
4.3 Subpixel Mapping Method Based on Multi-Shift With Spatial-Spectral Information
88(12)
4.3.1 Multi-Shift Image With More Spatial-Spectral Information
88(3)
4.3.2 Experiment Content and Result Analysis
91(9)
4.4 Subpixel Mapping Based on the Spatial Attraction Model With Multi-Scale Subpixel Shifted Images
100(15)
4.4.1 Subpixel-Pixel Spatial Attraction Model
100(2)
4.4.2 Subpixel-Subpixel Spatial Attraction Model
102(1)
4.4.3 Spatial Attraction Model With Multi-Scale Subpixel Shifted Image
103(1)
4.4.4 Experiment Content and Result Analysis
104(11)
4.5 Utilizing Parallel Networks to Produce Subpixel Shifted Images With Multi-Scale Spatial-Spectral Information for Subpixel Mapping
115(17)
4.5.1 Multi-Scale Network and Spatial-Spectral Network
115(4)
4.5.2 Multi-Scale Spatial-Spectral Information
119(2)
4.5.3 Experimental Content and Result Analysis
121(11)
4.6 Spatiotemporal Subpixel Mapping by Considering the Point Spread Function Effect
132(10)
4.6.1 Spatial Dependence
133(2)
4.6.2 Temporal Dependence
135(1)
4.6.3 Spatiotemporal Dependence
136(1)
4.6.4 Experimental Content and Result Analysis
136(6)
4.7 Summary
142(3)
References
143(2)
Chapter 5 Subpixel Mapping of Remote Sensing Image Based on Fusion Technology
145(46)
5.1 Introduction
145(1)
5.2 Soft-Then-Hard Subpixel Mapping Based on Pansharpening Technology
146(13)
5.2.1 Pansharpening Technology
146(2)
5.2.2 STHSRM-PAN
148(2)
5.2.3 Experimental Content and Result Analysis
150(9)
5.3 Subpixel Land Cover Mapping Based on Parallel Processing Path for Hyperspectral Image
159(16)
5.3.1 Fusion Path
159(2)
5.3.2 Deep Learning Path
161(2)
5.3.3 Dual Processing Path
163(1)
5.3.4 Experimental Content and Result Analysis
164(11)
5.4 Subpixel Mapping Based on Multi-Source Remote Sensing Fusion Data for Land Cover Classes
175(11)
5.4.1 Data-Level Fusion
178(1)
5.4.2 Feature Fusion
178(1)
5.4.3 Obtaining Mapping Result
179(1)
5.4.4 Experimental Content and Result Analysis
180(6)
5.5 Summary
186(5)
References
188(3)
Chapter 6 Remote Sensing Image Subpixel Mapping Based on Classification Then Reconstruction
191(36)
6.1 Introduction
191(1)
6.2 Theoretical Basis
191(8)
6.2.1 Super-Resolution Algorithm
191(2)
6.2.2 Fully Supervised Information Classification Algorithm
193(6)
6.3 Subpixel Mapping Based on MAP Super-Resolution Reconstruction Then Classification
199(20)
6.3.1 Transformed MAP-Based Super-Resolution Reconstruction
199(4)
6.3.2 LSSVM Classification Algorithm
203(1)
6.3.3 Experiment Content and Result Analysis
204(15)
6.4 Subpixel Mapping Based on Pansharpening Then Classification
219(4)
6.4.1 Implementation Steps
219(1)
6.4.2 Experiment Content and Result Analysis
220(3)
6.5 Summary
223(4)
References
224(3)
Chapter 7 Application of Subpixel Mapping Technology in Remote Sensing Imaging
227(32)
7.1 Introduction
227(1)
7.2 Improving Flood Subpixel Mapping for Multispectral Image by Supplying More Spectral Information
228(7)
7.2.1 Existing SRFIM
228(2)
7.2.2 SRFIM-MSI
230(1)
7.2.3 Experiment Content and Result Analysis
231(4)
7.3 Subpixel Mapping of Urban Buildings Based in Multispectral Image With Spatial-Spectral Information
235(6)
7.3.1 Spaceborne Multispectral Remote Sensing Image
235(1)
7.3.2 Experiment Content and Result Analysis
236(5)
7.4 Multispectral Subpixel Burned-Area Mapping Based on Space-Temperature Information
241(15)
7.4.1 Space Part
241(2)
7.4.2 Temperature Part
243(1)
7.4.3 Implementation of STI
244(1)
7.4.4 Experiment Content and Result Analysis
245(11)
7.5 Summary
256(3)
References
256(3)
Appendix: Abbreviations 259(6)
Content Validity 265(2)
Index 267
Peng Wang earned his Ph.D. from the College of Information and Communications Engineering, Harbin Engineering University, Harbin, China, in 2018. He is currently an associate professor at the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Jiangsu, China. His research interests include remote sensing imagery processing and machine learning. He has authored one book and more than 50 papers.

Lei Zhang earned his Ph.D. from the Graduate School of Chinese Academy of Sciences in 2008 and finished the postdoctoral program at Tsinghua University in 2010. From 2011 to 2012 he was an associate professor at the Chinese University of Hong Kong, and from 2012 to 2015 at the Shanghai Institute of Technical Physics of Chinese Academy of Sciences. Now he is a professor in Tongji University. His research interests include intelligent information processing and spatio-temporal applications.