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E-raamat: Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification [Taylor & Francis e-raamat]

, (Centre for Space Science and Technology Education in Asia and the Pacific, Dehradun, INDIA), (London Metropolitan University, UK)
  • Formaat: 194 pages, 15 Tables, black and white; 64 Illustrations, black and white
  • Ilmumisaeg: 20-Jul-2020
  • Kirjastus: CRC Press
  • ISBN-13: 9780429340369
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  • Taylor & Francis e-raamat
  • Hind: 147,72 €*
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Fuzzy Machine Learning Algorithms for Remote Sensing Image ClassificationSuurem pilt
  • Formaat: 194 pages, 15 Tables, black and white; 64 Illustrations, black and white
  • Ilmumisaeg: 20-Jul-2020
  • Kirjastus: CRC Press
  • ISBN-13: 9780429340369
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429340369
This book covers the state-of-art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date, multi-sensor remote sensing are capable to monitor specific stage (for e.g., phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance, forest fire mapping, stubble burning, post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well, this book addresses other related algorithms based on distance, kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels, non-linearity and noisy pixels.

Further, this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate, postgraduate, research scholars and working professionals of different branches such as Geoinformation sciences, Geography, Electrical, Electronics and Computer Sciences etc., working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields, for example, in medical imaging. Overall, this book aims to:











exclusive focus on using large range of fuzzy classification algorithms for remote sensing images;





discuss ANN, CNN, RNN, and hybrid learning classifiers application on remote sensing images;





describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms;





explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool, Entropy, Correlation Coefficient, Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and;





combines explanation of the algorithms with case studies and practical applications.
Foreword xi
Preface xv
Our Gratitude with three R's xix
Authors xxi
List of Abbreviations
xxiii
Chapter 1 Machine Learning
1(8)
1.1 Introduction
1(1)
1.2 Machine Learning Approaches
1(3)
1.3 Understanding Pattern Recognition
4(1)
1.4 Machine Learning Applications and Examples
5(2)
1.5 Summary
7(1)
Bibliography
7(2)
Chapter 2 Ground Truth Data for Remote Sensing Image Classification
9(8)
2.1 Introduction
9(2)
2.2 Creation of Training Data
11(1)
2.3 Criteria for Ground Truth Data
12(1)
2.4 Training Data in Machine Learning
12(2)
2.5 Validation Dataset
14(1)
2.6 Testing Dataset
14(1)
2.7 Summary
15(1)
Bibliography
15(2)
Chapter 3 Fuzzy Classifiers
17(16)
3.1 Introduction
17(3)
3.1.1 Soft Classifiers
17(1)
3.1.2 Traditional Classifiers versus Soft Classifiers
18(1)
3.1.3 Linear and Nonlinear Classifiers
19(1)
3.2 Clustering Algorithms
20(10)
3.2.1 Fuzzy c-Means (FCM) Classifier
20(2)
3.2.2 Possibilistic c-Means (PCM) Classifier
22(2)
3.2.3 Noise Clustering (NC) Classifier
24(2)
3.2.3.1 Noise Clustering Algorithm
26(1)
3.2.3.2 Why Noise Clustering over PCM?
26(1)
3.2.3.3 Drawbacks of Possibilistic c-Means (PCM)
27(1)
3.2.4 Improved Possibilistic c-Means (IPCM)
27(1)
3.2.4.1 Advantages of IPCM over PCM
27(1)
3.2.4.2 Mathematical Formulation of IPCM
27(1)
3.2.4.3 Characteristic Features of IPCM
28(1)
3.2.5 Modified Possibilistic c-Means (MPCM)
29(1)
3.2.5.1 Mathematical Formulation of MPCM
29(1)
3.3 Summary
30(1)
Bibliography
30(3)
Chapter 4 Learning Based Classifiers
33(24)
4.1 Introduction
33(1)
4.2 Variants of Artificial Neural Network (ANN)
33(8)
4.2.1 Back-Propagation
38(1)
4.2.2 Weight Update
39(2)
4.3 Convolutional Neural Network (CNN)
41(5)
4.3.1 Convolutional Neural Networks Image Classification
41(2)
4.3.2 Supervised Machine Learning
43(3)
4.4 Recurrent Neural Network (RNN)
46(1)
4.5 Hybrid Learning Network (HLN)
47(1)
4.5.1 Training Issues -- Remote Sensing Data Domain
48(1)
4.6 Deep Learning Concepts
48(2)
4.6.1 Challenges in Learning Algorithms
49(1)
4.7 In-house Tool for Study of Learning Algorithms
50(3)
4.8 Summary
53(1)
Bibliography
54(3)
Chapter 5 Hybrid Fuzzy Classifiers
57(38)
5.1 Introduction
57(1)
5.2 Hybrid Approach
57(5)
5.2.1 Entropy Based Hybrid Soft Classifiers
59(1)
5.2.2 Fuzzy c-Means with Entropy (FCME)
59(1)
5.2.3 Noise Clustering with Entropy (NCE) Classifier
60(2)
5.3 Similarity/Dissimilarity Measures in Fuzzy Classifiers
62(6)
5.3.1 Similarity Measures
63(1)
5.3.1.1 Cosine Similarity Measure
63(1)
5.3.1.2 Correlation Similarity Measure
63(1)
5.3.2 Dissimilarity Measures
64(1)
5.3.2.1 Euclidean Distance
65(1)
5.3.2.2 Manhattan Distance
65(1)
5.3.2.3 Chessboard
66(1)
5.3.2.4 Bray Curtis
66(1)
5.3.2.5 Canberra
66(1)
5.3.2.6 Mean Absolute Difference
67(1)
5.3.2.7 Median Absolute Difference
67(1)
5.3.2.8 Normalized Squared Euclidean
67(1)
5.3.2.9 Composite Measure: Combining Similarity and Dissimilarity Measures
68(1)
5.4 Spectral Characterization Measures
68(2)
5.4.1 Spectral Information Divergence (SID)
68(1)
5.4.2 Spectral Angle Mapper (SAM)
69(1)
5.4.3 Spectral Correlation Angle (SCA)
69(1)
5.5 Hybridization of Spectral Measures
70(1)
5.5.1 SID-SAM Hybridization
70(1)
5.5.2 SID-SCA Hybridization
70(1)
5.6 Kernels Concept in Fuzzy Classifiers
71(3)
5.6.1 Local Kernels
72(1)
5.6.2 Global Kernels
73(1)
5.6.3 Spectral Kernels
73(1)
5.6.4 Hybrid Kernel Approach
74(1)
5.7 Theory behind Markov Random Field (MRF)
74(7)
5.7.1 MAP-MRF Framework
75(1)
5.7.2 Contextual Information Using MRF
76(1)
5.7.3 Contextual Fuzzy Classifier
77(1)
5.7.4 Smoothness Prior
77(1)
5.7.5 Discontinuity Adaptive (DA) Priors
78(1)
5.7.5.1 Standard Regularization
79(1)
5.7.5.2 DA MRF Model
79(1)
5.7.5.3 How DA Priors Work
80(1)
5.8 Convolution Based Local Information in Fuzzy Classifiers
81(9)
5.8.1 Fuzzy c-Means with Constraints (FCM-S) Algorithm
82(1)
5.8.2 Possibilistic c-Means with Constraints (PCM-S) Algorithm
82(1)
5.8.3 Fuzzy Local Information c-Means (FLICM) Algorithm
83(1)
5.8.4 Possibilistic Local Information c-Means (PLICM) Algorithm
84(2)
5.8.5 Adaptive Fuzzy Logic Local Information c-Means (ADFLICM)
86(1)
5.8.6 Adaptive Possibilistic Local Information c-Means (ADPLICM) Algorithm
87(1)
5.8.7 Modified Possibilistic c-Means with Constraints (MPCM-S) Algorithm
88(1)
5.8.8 Modified Possibilistic Local Information c-Means (MPLICM) Algorithm
89(1)
5.8.9 Adaptive Modified Possibilistic Local Information c-Means (ADMPLICM) Algorithm
89(1)
5.9 Summary
90(1)
Bibliography
90(5)
Chapter 6 Fuzzy Classifiers for Temporal Data Processing
95(18)
6.1 Introduction
95(1)
6.2 Temporal Indices Approach
96(2)
6.3 Feature Selection Methods
98(1)
6.4 Some Case Studies for Temporal Data Analysis
99(4)
6.5 Single Class Extraction
103(5)
6.5.1 Fuzzy Set Theory Based Classifiers for a Single Class Extraction
103(5)
6.6 Concept for Mono-/Bi-sensor Remote Sensing Data Processing
108(1)
6.7 Summary
108(1)
Bibliography
108(5)
Chapter 7 Assessment of Accuracy for Soft Classification
113(18)
7.1 Introduction
113(2)
7.2 Generation of Testing Data
115(1)
7.3 Methods for Assessment of Accuracy of Soft Classified Outputs
115(12)
7.3.1 Fuzzy Error Matrix and Other Associated Operators
116(1)
7.3.1.1 Fuzzy Error Matrix
116(2)
7.3.1.2 Composite Operator Based FERM
118(2)
7.3.1.3 Sub-Pixel Confusion-Uncertainty Matrix (SCM)
120(3)
7.3.2 Measure of Uncertainty: Entropy
123(1)
7.3.3 Correlation Coefficient
124(1)
7.3.4 Root Mean Square Error
124(1)
7.3.5 Receiver Operating Characteristic (ROC)
125(1)
7.3.6 Method for Edge Preservation
126(1)
7.4 Summary
127(1)
Bibliography
127(4)
Appendix: A1 SMIC: Sub-Pixel Multi-Spectral Image Classifier Package 131(10)
Appendix: A2 Case Studies from SMIC Package 141(44)
Index 185
Anil Kumar is working as Scientist/Engineer-'SG & Head Photogrammetry and Remote Sensing Department at Indian Institute of Remote Sensing (IIRS), Indian Space Research organisation (ISRO), Dehradun, India. He received his B.Tech degree in Civil Engineering from University of Lucknow, India and M.E. degre as well as inservise Ph.D in soft computing from Indian Institute of Technology, Roorkee, India. He has published 46 papers in journals. Guided 36 masters and 5 Ph.D thesis. He has been recipient of the prestigious P. R. Pisharoty Memorial Award conferred by the Indian Society of Remote Sensing. He is a life member of the Indian Society of Remote Sensing. His current research interests are in Soft computing, Deep Learning, Multi-sensor temporal data processing, Digital Photogrammetry, GPS and LiDAR.

Priyadarshi Upadhyay is working as a Scientist/Engineer-SD in Uttarakhand Space Application Centre (USAC), Dehradun, India. He received his M.Sc. degree in Physics from Kumaun University Nainital, India and M.Tech. degree in Remote Sensing from Birla Institute of Technology, Mesra Ranchi, India. He has received his Ph.D. degree from Indian Institute of Technology Roorkee, India in the area of time series remote sensing for single crop identification. He has published 15 research papers in various International Journals, Internation and National Conferences. He has been awarded by presitigious CSIR-NET, GATE and MHRD Travel Grant Fellowships. He is a life member of Indian Society of Remote Sensing and The Institute of Engineers (India). His current research interest are Microwave Remote Sensing for Soil Moisture and Crop Mapping, Polarimatric and Inerferrometric SAR, Hyperspectral and Optical Remote Sensing, Climate Change, Ecological Studies in Himalayan Region for Economically Important Crops and Plants.

A. Senthil Kumar is the Director of UN-affliated Centre for Space Science and Technology Education in Asia and the Pacific in Dehradun, India. He received M.Sc. (Engg.) and Ph.D. from the Indian Institute of Science, Bangalore in the field of image processing in 1985 and 1990 respectively. He joined ISRO in 1991. Since then he has served in Indian Remote Sensing programs in various capacities. He has published more than 120 technical papers in international journals and conferences and co-edited a book on Remote Sensing of Northwest Himalayan Ecosystems. He has received ISRO Team awards for his contributions to Chandrayaan-1 and Cartosat-1 missions. His research areas include remote sensing sensor characterization, radiometric data processing, image restoration, data fusion techniques and in soft computing techniques. He has also been a recipient of the prestigious Prof. Satish Dhawan Award conferred by the Indian Society of Remote Sensing. He is a life member of the Indian Society of Remote Sensing and the Indian Society of Geomatics.
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