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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. This book 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.
Sommario
1. Introduction ¿ GeoAI: Challenges and Opportunities
Section I: Unsupervised, Supervised and Semi-Supervised Learning 2. A Review of Deep Neural Networks for Robust Analysis of Wide-Scale Geospatial Imagery 3. Advanced Deep Neural Network Architectures for GeoAI 4. Advances in Change and Anomaly Detection for Geospatial Imagery Data 5. Advances in Spectral Unmixing for Geospatial Image Analysis 6. Advances in Deep Semantic Segmentation for Remote Sensing 7. Compressive Deep Learning for Remote Sensing 8. Semi-Supervised and Active Deep Learning ¿ Geospatial Image Analysis with Limited Ground Truth 9. Visual Question Answering for Remotely Sensed Imagery
Section II: Multi-Modal GeoAI 10. Generative Adversarial Networks for Transfer Learning, Data Augmentation and Super-Resolution 11. Data Fusion Networks for Analysis of Multi-Modal Imagery Acquired from Multiple Heterogenous Sensors 12. Advances in Transfer Learning for Multi-Sensor, Multi-Temporal Earth Science Data 13. Advances in Image Super-Resolution for Analysis of Multi-Scale Earth Science Data 14. Harmonizing Contextual and Deep Features for Multi-Modal Geospatial Image Analysis
Section III: GeoAI in Practice 15. GeoAI for Earth Science - A NASA and ESA Perspective 16. GeoAI on the Cloud - State-of-the-Art Operational GeoAI Solutions on the Cloud for Earth Science 17. Deep Learning and Earth Observations in Support of Climate Science and Sustainable Development 18. Advances in Multi-Modal Machine Learning with Applications to Precision Agriculture
Info autore
Saurabh Prasad (Senior IEEE Member) is an Associate Professor with the Department of Electrical and Computer Engineering, University of Houston, USA, where he directs the Machine Learning and Signal Processing Laboratory. His lab focuses on advancing state-of-the-art in machine learning and signal processing with applications to remote sensing and biomedicine. His work has been recognized by two student research awards during his Ph.D. study, a best student paper award at the 2008 IGARSS conference, top-10% papers at IEEE-ICIP conference, a NASA New Investigator (Early Career) award in 2014, and the junior faculty research excellence award at the University of Houston in 2017. He was the lead book editor on two Springer books on signal processing and machine learning for hyperspectral image analysis.Jocelyn Chanussot (IEEE Fellow) has been with Grenoble INP since 1999, where he is currently a Professor of signal and image processing. His research interests include image analysis, hyperspectral remote sensing, data fusion, machine learning and artificial intelligence. He has been a visiting scholar at Stanford University (USA), KTH (Sweden) and NUS (Singapore) and is an Adjunct Professor of the University of Iceland. In 2015-2017, he was a visiting professor at the University of California, Los Angeles. He holds the AXA chair in remote sensing and is an Adjunct professor at the Chinese Academy of Sciences, Aerospace Information research Institute, Beijing. Dr. Chanussot is the founding President of IEEE Geoscience and Remote Sensing French chapter for which he which received the 2010 IEEE GRS-S Chapter Excellence Award. He was also the Vice-President of the IEEE Geoscience and Remote Sensing Society, in charge of meetings and symposia; the General Chair of the first IEEE GRSS Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote sensing; Chair and Cochair of the GRS Data Fusion Technical Committee.Jun Li (IEEE Fellow) is a Professor with the School of Geography and Planning, Sun Yat-sen University, Guangzhou, China. Since 2013, she has obtained several prestigious funding grants at the national and international level. Her research interests include remotely sensed hyperspectral image analysis, signal processing, supervised/semisupervised learning, and active learning. Dr. Li currently serves as the Editor-in-Chief for the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
