Earth Observation Data Analytics Using Machine and Deep Learning: Modern tools, applications and challenges

Cover
Contents
About the editors
Foreword
1 Introduction
1.1 Earth observation data
1.1.1 Organization
1.2 Categories of EO data
1.2.1 Passive imaging system
1.2.2 Active imaging system
1.3 Need of data analytics in EO data
1.4 Data analytics methodology
1.4.1 Machine learning
1.4.2 Deep learning
1.5 Data visualization techniques
1.5.1 Cartogram map
1.5.2 Heat map
1.5.3 Choropleth map
1.6 Types of inferences from data analytics (application areas)
1.6.1 Agriculture
1.6.2 Forestry
1.6.3 Land cover classification
1.6.4 Flooding
1.6.5 Maritime
1.6.6 Defence and security
1.6.7 Wetland
1.7 Conclusion
References
Part I. Clustering and classification of Earth Observation data
2 Deep learning method for crop classification using remote sensing data
2.1 Sources of remote sensing data collection
2.2 Tools for processing remote sensing data
2.3 Crop classification using remote sensing data
2.3.1 Methods for crop classification
2.3.2 Case study
2.4 Performance evaluation
2.5 Conclusion
References
3 Using optical images to demarcate fields in L band SAR images for effective deep learning based crop classification and crop cov
3.1 Introduction
3.1.1 Motivation
3.1.2 Research contribution
3.1.3 Organization
3.2 Related work
3.3 Proposed methodology
3.3.1 SAR image pre-processing and decomposition
3.3.2 Edge detection & field extraction
3.3.3 Classification using deep learning
3.4 Study area
3.5 Experimental setting
3.5.1 Dataset 1
3.5.2 Dataset 2
3.6 Experimental result and analysis
3.7 Conclusion
References
4 Leveraging twin networks for land use land cover classification
4.1 Introduction
4.2 Related literature
4.3 Methodology
4.3.1 Dataset
4.3.2 Siamese network
4.3.3 Encoders
4.4 Results and discussion
4.5 Conclusion and future work
References
5 Exploiting artificial immune networks for enhancing RS image classification
5.1 Introduction
5.1.1 The immune system
5.1.2 Classification based on the AIS
5.2 Data used and study area
5.3 Experimental approach
5.3.1 Initialization
5.3.2 Randomly choose an antigen
5.3.3 Select the
highest affinity
5.3.4 Clone the
selected Ab’s
5.3.5 Allow each Ab’s in clone set
5.3.6 Calculate the affinity aff * j
5.3.7 Select the highest affinity
5.3.8 Decide
5.3.9 Replace
5.3.10 A stopping criterion
5.4 Result
5.5 Conclusion
References
6 Detection and segmentation of aircrafts in UAV images with a deep learning-based approach
6.1 Introduction
6.2 Background
6.2.1 Digital images and spatial resolution
6.2.2 Neural networks
6.2.3 CNNs
6.3 Methodology
6.3.1 Dataset
6.3.2 Object detection
6.3.3 Semantic segmentation
6.4 Model training and results
6.4.1 Object detection
6.4.2 Semantic segmentation
6.5 Conclusions and discussion
References
Part II. Rare event detection using Earth Observation data
7 A transfer learning approach for hurricane damage assessment using satellite imagery
7.1 Introduction
7.2 Literature review
7.3 Image processing techniques
7.3.1 Statistical-based algorithms
7.3.2 Learning-based algorithms
7.4 Transfer learning
7.4.1 AlexNet
7.5 Implementation
7.6 Conclusion
References
8 Wildfires, volcanoes and climate change monitoring from satellite images using deep neural networks
8.1 Introduction
8.2 Background and related work
8.3 Modern DL methods
8.3.1 U-Net
8.3.2 AlexNet
8.3.3 Inception-v3
8.3.4 Other neural networks
8.4 Benefits of using this approach
8.5 Long-term climate change monitoring using DL methods
8.6 Other applications of this approach
8.7 Possible problems
8.8 Conclusion
References
9 A comparative study on torrential slide shortcoming zones and causative factors using machine learning techniques: a case study
9.1 Introduction
9.2 Discussions on landslide influencing factors
9.3 Materials and methods
9.4 Dataset collections
9.5 Rainfall characteristics in Kerala
9.6 Landslide impacted earthquake
9.7 Anthropogenic activities
9.8 Machine learning techniques for landslide study using satellite images
9.8.1 Highlights of machine learning techniques in satellite images
9.9 Emergency rescue and mitigation
9.10 Conclusion
References
10 Machine learning paradigm for predicting reservoir property: an exploratory analysis
10.1 Introduction
10.2 Geo-scientific data sources for reservoir characterization
10.2.1 Seismic survey
10.2.2 Well logging
10.3 Research issues and objectives
10.4 Description of the case study
10.4.1 Geological background of the survey area
10.5 ML for reservoir characterization: the proposed approach
10.5.1 Well tie
10.5.2 Seismic signal reconstruction
10.5.3 Smoothing of well log
10.5.4 Seismic attributes selection
10.5.5 Outlier removal
10.6 Experimental results and analysis
10.6.1 Statistical data analysis
10.6.2 Results and analysis of ML modeling
10.6.3 Performance comparison of shallow vs. DNN model
10.7 Discussion and future prospects
10.8 Conclusion
Acknowledgment
References
Part III. Tools and technologies for Earth Observation data
11 The application of R software in water science
11.1 Introduction
11.1.1 What is hydrology?
11.1.2 What is computational hydrology?
11.1.3 What is hydroinformatics?
11.1.4 Free, open-source software (FOSS)
11.1.5 What is GitHub?
11.2 Material and methods
11.2.1 What is R? What is an integrated development environment (IDE)?
11.2.2 What are R packages?
11.2.3 What are cheatsheets?
11.2.4 What are R communities?
11.2.5 What is RPubs?
11.2.6 What are popular conferences in R?
11.2.7 What is joss (open source software)?
11.2.8 What is R studio cloud?
11.2.9 What is R application in hydrology?
11.2.10 What are hydrological packages?
11.2.11 Workflow of R in hydrology
11.2.12 Data for hydrology? How to retrieve datasets?
11.2.13 Preprocessing retrieved hydrological data (data tidying)
11.2.14 Different hydrology model types?
11.2.15 Hydrologic time series analysis tools in R?
11.2.16 Hydrological ML application tools in R?
11.2.17 Remote sensing tools in R
11.3 Conclusion and future prospects
References
12 Geospatial big data analysis using neural networks
12.1 Introduction
12.1.1 Geospatial data
12.1.2 Big data analysis
12.1.3 Fog computing
12.1.4 Neural network
12.1.5 Contribution
12.2 Related works
12.2.1 Big data analysis on geospatial data
12.2.2 Data processing techniques in fog environment
12.3 Proposed work
12.4 Methodology and concepts
12.4.1 Data pre-processing on fog environment
12.4.2 Prediction on cloud environment using ANN
12.5 Results and discussion
12.6 Conclusion
References
13 Software framework for spatiotemporal data analysis and mining of earth observation data
13.1 Introduction
13.1.1 Visualization
13.1.2 Multidimensional analysis
13.1.3 Data mining
13.2 Related work
13.3 Challenges
13.4 The ST-DAME
13.4.1 Conceptual architecture of the framework
13.4.2 Proposed framework
13.4.3 ST-DAME in action
13.5 Result
13.5.1 Automated system
13.5.2 Customized system
13.6 Conclusion
References
14 Conclusion
14.1 Excerpts from various chapters
14.2 Issues and challenges
14.2.1 Collecting meaningful and real-time data
14.2.2 Data storage
14.2.3 Resolution; quality promotion
14.2.4 Budget limitations
14.2.5 Standardization
14.2.6 Lack of ground truth data
14.2.7 Processing and analysis
References
Index
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