Deep Learning for Multi-Sensor Earth Observation
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Deep Learning for Multi-Sensor Earth Observation
Saha, Sudipan
Elsevier - Health Sciences Division
02/2025
350
Mole
9780443264849
Pré-lançamento - envio 15 a 20 dias após a sua edição
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Section 1: Introduction to Multi-Sensor Data and Artificial Intelligence
1. Deep Learning for Multisensor Earth Observation: Introductory Notes
2. A Basic Introduction to Deep Learning
Section 2: Artificial Intelligence for Sensor-specific data analysis and fusion
3. Deep learning processing of remotely sensed multispectral images
4. Deep Learning and Hyperspectral Images
5. Synthetic Aperture Radar Image Analysis in Era of Deep Learning
6. Deep Learning with Lidar for Earth Observation
7. Several Sensors and Modalities
Section 3: Advanced Concepts and Architectures
8. Self-Supervised Learning for Multimodal Earth Observation Data
9. Vision Transformers and Multisensor Earth Observation
10. Graph Neural Networks for Multi-Sensor Earth Observation
11. Uncertainty Quantification in Deep Neural Networks for Multisensor Earth Observation
Section 4: Multi-sensor Deep Learning Applications
12. Multi-Sensor Deep Learning for Change Detection
13. Multi-Sensor Deep Learning for Glacier Mapping
14. Deep Learning in Multisensor Agriculture and Crop Management
15. Miscellaneous Applications of Deep Learning based Multisensor Earth Observation
16. Multi-Sensor Earth Observation: Outlook
1. Deep Learning for Multisensor Earth Observation: Introductory Notes
2. A Basic Introduction to Deep Learning
Section 2: Artificial Intelligence for Sensor-specific data analysis and fusion
3. Deep learning processing of remotely sensed multispectral images
4. Deep Learning and Hyperspectral Images
5. Synthetic Aperture Radar Image Analysis in Era of Deep Learning
6. Deep Learning with Lidar for Earth Observation
7. Several Sensors and Modalities
Section 3: Advanced Concepts and Architectures
8. Self-Supervised Learning for Multimodal Earth Observation Data
9. Vision Transformers and Multisensor Earth Observation
10. Graph Neural Networks for Multi-Sensor Earth Observation
11. Uncertainty Quantification in Deep Neural Networks for Multisensor Earth Observation
Section 4: Multi-sensor Deep Learning Applications
12. Multi-Sensor Deep Learning for Change Detection
13. Multi-Sensor Deep Learning for Glacier Mapping
14. Deep Learning in Multisensor Agriculture and Crop Management
15. Miscellaneous Applications of Deep Learning based Multisensor Earth Observation
16. Multi-Sensor Earth Observation: Outlook
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Calving front detection; Change detection; Convolutional neural network; Data fusion; Deep learning; Domain adaptation; Earth observation; Fusion; Generative adversarial network; Glacier extent mapping; Graph neural networks; Hyperspectral imaging; Image fusion; Multi-modal; Multi-modality; Multi-sensor; Multi-sensor Earth observation; Multi-spectral data; Multi-temporal analysis; Object detection; Recurrent neural network; Remote sensing; Scene classification; Segmentation; Self-supervised; Semantic segmentation; Semi-supervised learning; Super-resolution; Target detection; Uncertainty;
Section 1: Introduction to Multi-Sensor Data and Artificial Intelligence
1. Deep Learning for Multisensor Earth Observation: Introductory Notes
2. A Basic Introduction to Deep Learning
Section 2: Artificial Intelligence for Sensor-specific data analysis and fusion
3. Deep learning processing of remotely sensed multispectral images
4. Deep Learning and Hyperspectral Images
5. Synthetic Aperture Radar Image Analysis in Era of Deep Learning
6. Deep Learning with Lidar for Earth Observation
7. Several Sensors and Modalities
Section 3: Advanced Concepts and Architectures
8. Self-Supervised Learning for Multimodal Earth Observation Data
9. Vision Transformers and Multisensor Earth Observation
10. Graph Neural Networks for Multi-Sensor Earth Observation
11. Uncertainty Quantification in Deep Neural Networks for Multisensor Earth Observation
Section 4: Multi-sensor Deep Learning Applications
12. Multi-Sensor Deep Learning for Change Detection
13. Multi-Sensor Deep Learning for Glacier Mapping
14. Deep Learning in Multisensor Agriculture and Crop Management
15. Miscellaneous Applications of Deep Learning based Multisensor Earth Observation
16. Multi-Sensor Earth Observation: Outlook
1. Deep Learning for Multisensor Earth Observation: Introductory Notes
2. A Basic Introduction to Deep Learning
Section 2: Artificial Intelligence for Sensor-specific data analysis and fusion
3. Deep learning processing of remotely sensed multispectral images
4. Deep Learning and Hyperspectral Images
5. Synthetic Aperture Radar Image Analysis in Era of Deep Learning
6. Deep Learning with Lidar for Earth Observation
7. Several Sensors and Modalities
Section 3: Advanced Concepts and Architectures
8. Self-Supervised Learning for Multimodal Earth Observation Data
9. Vision Transformers and Multisensor Earth Observation
10. Graph Neural Networks for Multi-Sensor Earth Observation
11. Uncertainty Quantification in Deep Neural Networks for Multisensor Earth Observation
Section 4: Multi-sensor Deep Learning Applications
12. Multi-Sensor Deep Learning for Change Detection
13. Multi-Sensor Deep Learning for Glacier Mapping
14. Deep Learning in Multisensor Agriculture and Crop Management
15. Miscellaneous Applications of Deep Learning based Multisensor Earth Observation
16. Multi-Sensor Earth Observation: Outlook
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Calving front detection; Change detection; Convolutional neural network; Data fusion; Deep learning; Domain adaptation; Earth observation; Fusion; Generative adversarial network; Glacier extent mapping; Graph neural networks; Hyperspectral imaging; Image fusion; Multi-modal; Multi-modality; Multi-sensor; Multi-sensor Earth observation; Multi-spectral data; Multi-temporal analysis; Object detection; Recurrent neural network; Remote sensing; Scene classification; Segmentation; Self-supervised; Semantic segmentation; Semi-supervised learning; Super-resolution; Target detection; Uncertainty;
