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E-raamat: Multi-resolution Image Fusion in Remote Sensing

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 24-Jan-2019
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108683043
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Multi-resolution Image Fusion in Remote SensingSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 24-Jan-2019
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108683043
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Presenting new advances in the field, this text will be a valuable reference for the students and researchers of image processing, multi-spectral imaging and remote sensing. It discusses tools and techniques of multi resolution image fusion with the necessary mathematical background.

Written in an easy-to-follow approach, the text will help the readers to understand the techniques and applications of image fusion for remotely sensed multi-spectral images. It covers important multi-resolution fusion concepts along with the state-of-the-art methods including super resolution and multi stage guided filters. It includes in depth analysis on degradation estimation, Gabor Prior and Markov Random Field (MRF) Prior. Concepts such as guided filter and difference of Gaussian are discussed comprehensively. Novel techniques in multi-resolution fusion by making use of regularization are explained in detail. It also includes different quality assessment measures used in testing the quality of fusion. Real-life applications and plenty of multi-resolution images are provided in the text for enhanced learning.

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Written using clear and accessible language, this useful guide discusses fundamental concepts and practices of multi-resolution image fusion.
List of Figures
xi
List of Tables
xv
Preface xvii
Acknowledgments xix
1 Introduction
1(26)
1.1 Characteristics of Remotely Sensed Imagery
3(11)
1.1.1 Multi-spectral images
8(2)
1.1.2 Panchromatic image
10(2)
1.1.3 Hyper-spectral images
12(2)
1.2 Low Spatial Resolution Imaging
14(3)
1.3 Image Fusion in Remotely Sensed Images
17(1)
1.4 Multi-resolution Image Fusion: An Ill-posed Inverse Problem
18(2)
1.5 Indian Remote Sensing Satellites
20(2)
1.6 Applications of Image Fusion
22(3)
1.7 Motivation
25(1)
1.8 Organization of the Book
25(2)
2 Literature Review
27(25)
2.1 Projection Substitution Based Techniques
29(5)
2.2 Multi-resolution Based Techniques
34(7)
2.3 Model Based Fusion Approaches
41(7)
2.4 Hyper-spectral Sharpening Methods
48(2)
2.5 Conclusion
50(2)
3 Image Fusion Using Different Edge-preserving Filters
52(28)
3.1 Related Work
53(1)
3.2 Fusion Using Multistage Guided Filter (MGF)
54(5)
3.2.1 Multistage guided filter (MGF)
55(2)
3.2.2 Proposed approach using guided filter
57(2)
3.3 Fusion Approach Using Difference of Gaussians (DoGs)
59(4)
3.3.1 Difference of Gaussians (DoGs)
60(1)
3.3.2 Proposed approach using DoGs
61(2)
3.4 Experimental Illustrations
63(15)
3.4.1 Experimentations: Ikonos-2 dataset
67(4)
3.4.2 Experimentations: Quickbird dataset
71(3)
3.4.3 Experimentations: Worldview-2 dataset
74(4)
3.4.4 Computational complexity
78(1)
3.5 Conclusion
78(2)
4 Image Fusion: Model Based Approach with Degradation Estimation
80(60)
4.1 Previous Works
81(2)
4.2 Description of the Proposed Approach Using Block Schematic
83(2)
4.3 Background: Contourlet Transform (CT)
85(1)
4.4 Contourlet Transform Based Initial Approximation
85(3)
4.5 Forward Model and Degradation Estimation
88(3)
4.6 MRF Prior Model
91(3)
4.7 MAP Estimation and Optimization Process
94(2)
4.7.1 MAP estimation
94(1)
4.7.2 Optimization process
95(1)
4.8 Experimentations
96(43)
4.8.1 Effect of decimation matrix coefficients on fusion
100(2)
4.8.2 Effect of MRF parameter γm on fusion
102(1)
4.8.3 Fusion results for degraded dataset: Ikonos-2
103(9)
4.8.4 Fusion results for degraded dataset: Quickbird
112(8)
4.8.5 Fusion results for degraded dataset: Worldview-2
120(6)
4.8.6 Fusion results for un-degraded (original) datasets: Ikonos-2, Quickbird and Worldview-2
126(7)
4.8.7 Spectral distortion at edge pixels
133(4)
4.8.8 Computational time
137(2)
4.9 Conclusion
139(1)
5 Use of Self-similarity and Gabor Prior
140(40)
5.1 Related Work
141(2)
5.2 Block Schematic of the Proposed Method
143(1)
5.3 Initial HR Approximation
144(6)
5.4 LR MS Image Formation Model and Degradation Matrix Estimation
150(2)
5.5 Regularization Using Gabor and MRF Priors
152(4)
5.5.1 Optimization process
155(1)
5.6 Experimental Results
156(23)
5.6.1 Experimental setup
158(1)
5.6.2 Experimental results on degraded and un-degraded Ikonos-2 datasets
159(5)
5.6.3 Experimental results on degraded and un-degraded Quickbird datasets
164(5)
5.6.4 Experimental results on degraded and un-degraded Worldview-2 datasets
169(5)
5.6.5 Comparison of fusion results with CS and TV based approaches
174(4)
5.6.6 Computation complexity
178(1)
5.7 Conclusion
179(1)
6 Image Fusion: Application to Super-resolution of Natural Images
180(23)
6.1 Related Work
181(3)
6.2 Estimation of Close Approximation of the SR Image
184(4)
6.3 Refining SR Using MAP--MRF Framework
188(2)
6.4 MRF Prior and SR Regularization
190(2)
6.4.1 Optimization process
191(1)
6.5 Experimental Demonstrations
192(10)
6.5.1 SR results on gray scale images
193(4)
6.5.2 SR results on color images
197(5)
6.6 Conclusion
202(1)
7 Conclusion and Directions for Future Research
203(8)
7.1 Conclusion
203(4)
7.2 Future Research Work
207(4)
Bibliography 211
Manjunath V. Joshi has received his M.Tech. and Ph.D. degrees from the Indian Institute of Technology (IIT) Bombay. Currently, he is serving as a Professor with the Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, India. He has been involved in active research in the areas of computer vision, machine learning and cognitive radio and has several publications in quality journals and conferences. He has co-authored two books entitled Motion-Free Super Resolution (2005) and Digital Heritage Reconstruction using Super-resolution and Inpainting (2016). Dr Joshi has received Outstanding Researcher Award in Engineering by the Research Scholars Forum of IIT Bombay and Dr Vikram Sarabhai Award for 20062007. Kishor P. Upla is Assistant Professor in Sardar Vallabhbhai National Institute of Technology, Surat (SVNIT). He received his Ph.D. degree from Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, India. He has fourteen years of teaching experience. His areas of interest include signal and image processing, multi-spectral and hyperspectral image analysis.
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