Satellite image analysis: clustering and classification

✍ Scribed by Borra, Surekha; Dey, Nilanjan; Thanki, Rohit

• Publisher: Springer
• Year: 2019
• Tongue: English
• Leaves: 110
• Series: SpringerBriefs in Applied Sciences and Technology Ser
• Category: Library

✦ Synopsis

Intro; About the Book; Contents; About the Authors; List of Figures; List of Tables; 1 Introduction; 1.1 Introduction; 1.2 Satellite Imaging Sensors; 1.3 Panchromatic and Multispectral Images; 1.4 Resolution in Satellite Images; 1.5 Distortions in Satellite Images; 1.6 Manual Versus Automatic Interpretation; 1.7 Classification and Clustering; 1.8 Performance Evaluation of Classification Techniques; 1.9 Conclusion; References; 2 Satellite Image Enhancement and Analysis; 2.1 Satellite Image Degradation and Restoration; 2.2 Geometric Correction or Rectification in Satellite Images

✦ Table of Contents

About the Book......Page 6
Contents......Page 7
About the Authors......Page 10
List of Figures......Page 12
List of Tables......Page 14
1.1 Introduction......Page 16
1.3 Panchromatic and Multispectral Images......Page 17
1.5 Distortions in Satellite Images......Page 19
1.6 Manual Versus Automatic Interpretation......Page 20
1.7 Classification and Clustering......Page 21
1.8 Performance Evaluation of Classification Techniques......Page 22
References......Page 26
2.2 Geometric Correction or Rectification in Satellite Images......Page 28
2.4 Satellite Image Enhancement......Page 30
2.5 Satellite Image Segmentation......Page 34
2.6 Image Stitching......Page 37
2.7 Satellite Image Interpolation......Page 38
2.8 Multivariate Image Processing......Page 39
2.10 Band Ratioing......Page 40
2.11 Other Image Transformations......Page 41
References......Page 43
3.1 Introduction......Page 45
3.2 Supervised Classification......Page 47
3.3 Unsupervised Classification (Clustering)......Page 48
3.5 Iterative Self-organizing Data Analysis (ISODATA)......Page 50
3.6 Gaussian Mixture Models......Page 52
3.7 Self-organizing Maps......Page 54
3.8 Hidden Markov Models......Page 56
3.9 Feature Extraction and Dimensionality Reduction......Page 58
3.10 Conclusion......Page 61
References......Page 62
4.2 Supervised Classification......Page 67
4.3 Max Likelihood Classifier......Page 70
4.4 Naïve Bayes......Page 72
4.5 K-Nearest Neighbors (KNN)......Page 74
4.6 Minimum Distance to Means (MDM)......Page 76
4.7 Parallelepiped Classifier......Page 77
4.8 Support Vector Machine (SVM)......Page 78
4.9 Discriminant Analysis (DA)......Page 81
4.10 Decision Trees......Page 82
4.11 Binary Encoding Classification......Page 84
4.12 Spectral Angle Mapper Classification......Page 85
4.14 Deep Learning (DL)......Page 86
4.15 The Hybrid Approaches......Page 88
4.16 Semi-supervised Learning......Page 89
4.17 Challenges......Page 90
References......Page 91
5.1 Introduction......Page 96
5.2 Agriculture......Page 97
5.4 Rainfall Estimation......Page 98
5.5 Disaster Monitoring and Emergency Mapping......Page 99
5.6 Biodiversity......Page 100
5.7 Epidemiological Study......Page 101
5.10 Coastal Zone Management......Page 102
5.11 Road Detection......Page 103
5.12 Vehicle Detection......Page 104
5.15 Meteorology......Page 105
References......Page 106

✦ Subjects

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