Advances in Machine Learning and Image Analysis for GeoAI provides state-of-the-art machine learning and signal processing techniques for a comprehensive collection of geospatial sensors and sensing platforms. The book covers supervised, semi-supervised and unsupervised geospatial image analysis, sensor fusion across modalities, image super-resolution, transfer learning across sensors and time-points, and spectral unmixing among other topics. The chapters in these thematic areas cover a variety of algorithmic frameworks such as variants of convolutional neural networks, graph convolutional networks, multi-stream networks, Bayesian networks, generative adversarial networks, transformers and more.Advances in Machine Learning and Image Analysis for GeoAI provides graduate students, researchers and practitioners in the area of signal processing and geospatial image analysis with the latest techniques to implement deep learning strategies in their research.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Deep learning for super-resolution in remote sensing - p. 5 3. Chapter 3 Few-shot open-set recognition of hyperspectral images - p. 27 4. Chapter 4 Deep semantic segmentation networks for GeoAI: Impact of design choices on segmentation performance - p. 55 5. Chapter 5 Estimation of class priors for improving classification accuracy during deployment - p. 71 6. Chapter 6 Benchmarking and end-to-end considerations for GeoAI-enabled decision-making - p. 93 7. Chapter 7 Explainable AI for Earth observation: current methods, open challenges, and opportunities - p. 115 8. Chapter 8 Self-supervised contrastive learning for wildfire detection: utility and limitations - p. 153 9. Chapter 9 Multimodal deep learning for GeoAI - p. 165 10. Chapter 10 The power of voting - p. 201 11. Chapter 11 Visual question answering on remote sensing images - p. 237 12. Chapter 12 Spectral unmixing for geospatial image analysis - p. 255 13. Chapter 13 Applying GeoAI for effective large-scale wetland monitoring - p. 281 14. Chapter 14 Leveraging ML approaches for scaling climate data in an atmospheric urban digital twin framework - p. 315 15. Index - p. 347
Advances in Machine Learning and Image Analysis for GeoAI provides state-of-the-art machine learning and signal processing techniques for a comprehensive collection of geospatial sensors and sensing platforms. The book covers supervised, semi-supervised and unsupervised geospatial image analysis, sensor fusion across modalities, image super-resolution, transfer learning across sensors and time-points, and spectral unmixing among other topics. The chapters in these thematic areas cover a variety of algorithmic frameworks such as variants of convolutional neural networks, graph convolutional networks, multi-stream networks, Bayesian networks, generative adversarial networks, transformers and more.Advances in Machine Learning and Image Analysis for GeoAI provides graduate students, researchers and practitioners in the area of signal processing and geospatial image analysis with the latest techniques to implement deep learning strategies in their research.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Deep learning for super-resolution in remote sensing - p. 5 3. Chapter 3 Few-shot open-set recognition of hyperspectral images - p. 27 4. Chapter 4 Deep semantic segmentation networks for GeoAI: Impact of design choices on segmentation performance - p. 55 5. Chapter 5 Estimation of class priors for improving classification accuracy during deployment - p. 71 6. Chapter 6 Benchmarking and end-to-end considerations for GeoAI-enabled decision-making - p. 93 7. Chapter 7 Explainable AI for Earth observation: current methods, open challenges, and opportunities - p. 115 8. Chapter 8 Self-supervised contrastive learning for wildfire detection: utility and limitations - p. 153 9. Chapter 9 Multimodal deep learning for GeoAI - p. 165 10. Chapter 10 The power of voting - p. 201 11. Chapter 11 Visual question answering on remote sensing images - p. 237 12. Chapter 12 Spectral unmixing for geospatial image analysis - p. 255 13. Chapter 13 Applying GeoAI for effective large-scale wetland monitoring - p. 281 14. Chapter 14 Leveraging ML approaches for scaling climate data in an atmospheric urban digital twin framework - p. 315 15. Index - p. 347
