Borra / Thanki / Dey Satellite Image Analysis: Clustering and Classification

1. Auflage 2019
ISBN: 978-981-13-6424-2
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 97 Seiten

Reihe: SpringerBriefs in Applied Sciences and Technology

Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. 

This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.

Autoren/Hrsg.

Borra, Surekha

Thanki, Rohit

Dey, Nilanjan

Inhaltsverzeichnis

1;About the Book;6
2;Contents;7
3;About the Authors;10
4;List of Figures;12
5;List of Tables;14
6;1 Introduction;16
6.1;1.1 Introduction;16
6.2;1.2 Satellite Imaging Sensors;17
6.3;1.3 Panchromatic and Multispectral Images;17
6.4;1.4 Resolution in Satellite Images;19
6.5;1.5 Distortions in Satellite Images;19
6.6;1.6 Manual Versus Automatic Interpretation;20
6.7;1.7 Classification and Clustering;21
6.8;1.8 Performance Evaluation of Classification Techniques;22
6.9;1.9 Conclusion;26
6.10;References;26
7;2 Satellite Image Enhancement and Analysis;28
7.1;2.1 Satellite Image Degradation and Restoration;28
7.2;2.2 Geometric Correction or Rectification in Satellite Images;28
7.3;2.3 Noise Removal;30
7.4;2.4 Satellite Image Enhancement;30
7.5;2.5 Satellite Image Segmentation;34
7.6;2.6 Image Stitching;37
7.7;2.7 Satellite Image Interpolation;38
7.8;2.8 Multivariate Image Processing;39
7.9;2.9 Image Differencing;40
7.10;2.10 Band Ratioing;40
7.11;2.11 Other Image Transformations;41
7.12;References;43
8;3 Satellite Image Clustering;45
8.1;3.1 Introduction;45
8.2;3.2 Supervised Classification;47
8.3;3.3 Unsupervised Classification (Clustering);48
8.4;3.4 K-means Clustering;50
8.5;3.5 Iterative Self-organizing Data Analysis (ISODATA);50
8.6;3.6 Gaussian Mixture Models;52
8.7;3.7 Self-organizing Maps;54
8.8;3.8 Hidden Markov Models;56
8.9;3.9 Feature Extraction and Dimensionality Reduction;58
8.10;3.10 Conclusion;61
8.11;References;62
9;4 Satellite Image Classification;67
9.1;4.1 Introduction;67
9.2;4.2 Supervised Classification;67
9.3;4.3 Max Likelihood Classifier;70
9.4;4.4 Naïve Bayes;72
9.5;4.5 K-Nearest Neighbors (KNN);74
9.6;4.6 Minimum Distance to Means (MDM);76
9.7;4.7 Parallelepiped Classifier;77
9.8;4.8 Support Vector Machine (SVM);78
9.9;4.9 Discriminant Analysis (DA);81
9.10;4.10 Decision Trees;82
9.11;4.11 Binary Encoding Classification;84
9.12;4.12 Spectral Angle Mapper Classification;85
9.13;4.13 Artificial Neural Network (ANN);86
9.14;4.14 Deep Learning (DL);86
9.15;4.15 The Hybrid Approaches;88
9.16;4.16 Semi-supervised Learning;89
9.17;4.17 Challenges;90
9.18;References;91
10;5 Applied Examples;96
10.1;5.1 Introduction;96
10.2;5.2 Agriculture;97
10.3;5.3 Forestry;98
10.4;5.4 Rainfall Estimation;98
10.5;5.5 Disaster Monitoring and Emergency Mapping;99
10.6;5.6 Biodiversity;100
10.7;5.7 Epidemiological Study;101
10.8;5.8 Oceanography;102
10.9;5.9 Maritime/Illegal Fishing;102
10.10;5.10 Coastal Zone Management;102
10.11;5.11 Road Detection;103
10.12;5.12 Vehicle Detection;104
10.13;5.13 Aircraft Detection;105
10.14;5.14 Thermal Applications;105
10.15;5.15 Meteorology;105
10.16;5.16 Heritage Management;106
10.17;5.17 Challenges and Future Perspectives;106
10.18;References;106