Description
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.- Covers the latest machine learning and signal processing techniques that can effectively leverage multimodal geospatial imagery at scale- Chapters cover a variety of algorithmic frameworks pertaining to GeoAI, including superresolution, self-supervised learning, data fusion, explainable AI, among others- Presents cutting-edge deep learning architectures optimized for a wide array of geospatial imagery
Table of Contents
1. Introduction2. Deep Learning for Super-resolution in Remote Sensing3. Few-Shot Open-Set Recognition of Hyperspectral Images4. Deep Semantic Segmentation Networks for GeoAI: Impact of Design Choices on Segmentation Performance5. Estimation of Class Priors for Improving Classification Accuracy6. Benchmarking and end-to-end considerations for GeoAI-enabled decision making7. Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities8. Self-supervised Contrastive Learning for Wildfire Detection: Utility and Limitations9. Multi-Modal Deep Learning for GeoAI10. The Power of Voting - Ensemble Learning in Remote Sensing11. Language and Remote Sensing12. Spectral Unmixing for Geospatial Image Analysis13. Applying GeoAI for Effective Large-Scale Wetland Monitoring14. Leveraging ML approaches for scaling climate data in an atmospheric urban digital twin framework
