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Satellite Image Analysis: Clustering and Classification
Surekha Borra, Rohit Thanki, Nilanjan Dey
Verlag Springer-Verlag, 2019
ISBN 9789811364242 , 110 Seiten
Format PDF, OL
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Geräte
About the Book
6
Contents
7
About the Authors
10
List of Figures
12
List of Tables
14
1 Introduction
16
1.1 Introduction
16
1.2 Satellite Imaging Sensors
17
1.3 Panchromatic and Multispectral Images
17
1.4 Resolution in Satellite Images
19
1.5 Distortions in Satellite Images
19
1.6 Manual Versus Automatic Interpretation
20
1.7 Classification and Clustering
21
1.8 Performance Evaluation of Classification Techniques
22
1.9 Conclusion
26
References
26
2 Satellite Image Enhancement and Analysis
28
2.1 Satellite Image Degradation and Restoration
28
2.2 Geometric Correction or Rectification in Satellite Images
28
2.3 Noise Removal
30
2.4 Satellite Image Enhancement
30
2.5 Satellite Image Segmentation
34
2.6 Image Stitching
37
2.7 Satellite Image Interpolation
38
2.8 Multivariate Image Processing
39
2.9 Image Differencing
40
2.10 Band Ratioing
40
2.11 Other Image Transformations
41
References
43
3 Satellite Image Clustering
45
3.1 Introduction
45
3.2 Supervised Classification
47
3.3 Unsupervised Classification (Clustering)
48
3.4 K-means Clustering
50
3.5 Iterative Self-organizing Data Analysis (ISODATA)
50
3.6 Gaussian Mixture Models
52
3.7 Self-organizing Maps
54
3.8 Hidden Markov Models
56
3.9 Feature Extraction and Dimensionality Reduction
58
3.10 Conclusion
61
References
62
4 Satellite Image Classification
67
4.1 Introduction
67
4.2 Supervised Classification
67
4.3 Max Likelihood Classifier
70
4.4 Naïve Bayes
72
4.5 K-Nearest Neighbors (KNN)
74
4.6 Minimum Distance to Means (MDM)
76
4.7 Parallelepiped Classifier
77
4.8 Support Vector Machine (SVM)
78
4.9 Discriminant Analysis (DA)
81
4.10 Decision Trees
82
4.11 Binary Encoding Classification
84
4.12 Spectral Angle Mapper Classification
85
4.13 Artificial Neural Network (ANN)
86
4.14 Deep Learning (DL)
86
4.15 The Hybrid Approaches
88
4.16 Semi-supervised Learning
89
4.17 Challenges
90
References
91
5 Applied Examples
96
5.1 Introduction
96
5.2 Agriculture
97
5.3 Forestry
98
5.4 Rainfall Estimation
98
5.5 Disaster Monitoring and Emergency Mapping
99
5.6 Biodiversity
100
5.7 Epidemiological Study
101
5.8 Oceanography
102
5.9 Maritime/Illegal Fishing
102
5.10 Coastal Zone Management
102
5.11 Road Detection
103
5.12 Vehicle Detection
104
5.13 Aircraft Detection
105
5.14 Thermal Applications
105
5.15 Meteorology
105
5.16 Heritage Management
106
5.17 Challenges and Future Perspectives
106
References
106
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