Earth Observation Data Analytics Using Machine and Deep Learning Modern tools, applications and challenges edited by Sanjay Garg, Swati Jain, Nitant Dube and Nebu Varghese

Format:

Book

Author/Creator:

Garg, Sanjay

Contributor:

Jain, Swati, editor.

Dube, Nitant, editor.

Varghese, Nebu, editor.

Series:

IET computing series 56

IET computing series ;$v56

Language:

English

Subjects (All):

Remote sensing--Data processing.

Remote sensing.

Machine learning.

Physical Description:

1 online resource

Place of Publication:

London, United Kingdom 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

References

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

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

Notes:

Description based upon print version of record

6.4.1 Object detection

Other Format:

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

ISBN:

9781839536182

1839536187

9781837244225

1837244227

OCLC:

1382694031

Access Restriction:

Restricted for use by site license