Earth Observation Data Analytics Using Machine and Deep Learning : Modern Tools, Applications and Challenges.

Format:

Book

Author/Creator:

Garg, Sanjay.

Contributor:

Jain, Swati.

Dube, Nitant.

Varghese, Nebu.

Series:

Computing and Networks Series

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource (231 pages)

Edition:

1st ed.

Place of Publication:

Stevenage : Institution of Engineering & Technology, 2023.

Summary:

Using machine and deep learning techniques the authors introduce pre-processing methods applied to satellite images to identify land cover features, detect object, classify crops, recognize targets, and monitor and support earth resources. Readers will need a basic understanding of computing, remote sensing and image interpretation.

Contents:

Intro

Title

Copyright

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

3 Using optical images to demarcate fields in L band SAR images for effective deep learning based crop classification and crop cover estimation

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 &amp

amp

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

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

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 n highest affinity

5.3.4 Clone the n 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

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

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

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

9 A comparative study on torrential slide shortcoming zones and causative factors using machine learning techniques: a case study of an Indian state

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

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

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

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

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

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

Index.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Garg, Sanjay Earth Observation Data Analytics Using Machine and Deep Learning

ISBN:

9781839536182

1839536187

OCLC:

1389610031