Description
GeoAI for Earth Observation Imagery: Fundamentals and Practical Applications comprehensively covers methodologies of AI and Machine Learning applications of image processing for Earth Observation (EO) Imagery. As traditional image processing methods face challenges with handling vast volumes of EO imagery, leading to efficiencies and limitations when extracting meaningful insights, AI-driven approaches can enhance the efficiency, accuracy, and scalability of image processing. Chapters cover essential methodologies including atmospheric compensation, image enhancement techniques like deblurring and superresolution, and advanced analysis methods such as semantic segmentation and object detection.Cutting-edge approaches to computing, automating, and optimizing image processing tasks are also covered. Additionally, emerging trends in GeoAi and their implication on future research are reviewed. The book serves as an essential guide for navigating the complexities of spatial data and equips readers with knowledge to enhance their analytical capabilities.- Examines the essentials of image preprocessing, enhancement, analysis, and computing techniques tailored for EO imagery- Provides an introductory resource for implementing AI for EO image analysis- Demonstrates practical deployment of GeoML methodologies through case studies
Table of Contents
Part I – Image PreprocessingChapter 1. Earth observation and GeoAI through the years: six decades of progress in image analysisChapter 2. Radiometric correctionChapter 3. RectificationChapter 4. Georeferencing of remote sensing imageryChapter 5. Image registrationChapter 6. Mosaicking remote sensing data: techniques, challenges, and innovations [LUNGAD_FM_Q | PDF]Part II – Image EnhancementChapter 7. PansharpeningChapter 8. Superresolution of satellite imageryChapter 9. Earth observation image denoising [LUNGAD_FM_Q | PDF]Part III – Image AnalysisChapter 10. Semantic segmentation of Earth observation dataChapter 11. Synthesis of Earth observation imageryChapter 12. Geospatial data visualization with PythonChapter 13. Multimodal data fusion for semantic mapping and change detectionChapter 14. Self-supervised learning for Earth observation foundation modelsChapter 15. Object detection in remote sensingChapter 16. A tour of visual question answering for remote sensing [LUNGAD_FM_Q | PDF]Part IV – ComputingChapter 17. Geospatial machine learning librariesChapter 18. High-performance computing for geospatial intelligenceChapter 19. Cloud infrastructure for Earth observation imagery
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