| Description |
1 online resource (334 p.) |
| Series |
Computing and Networks Series |
|
Computing and Networks Series
|
| 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 |
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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 |
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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 & -- 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 |
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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 n highest affinity |
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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 -- 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 |
| 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 |
| Notes |
Description based upon print version of record |
|
6.4.1 Object detection |
| Form |
Electronic book
|
| Author |
Jain, Swati
|
|
Dube, Nitant
|
|
Varghese, Nebu
|
| ISBN |
9781839536182 |
|
1839536187 |
|
