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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. ix 7. Chapter 1: Earth observation and GeoAI through the years: six decades of progress in image analysis - p. 1 8. Part I: Image preprocessing - p. P1 9. Chapter 2: Radiometric correction - p. 9 10. Chapter 3: Rectification - p. 39 11. Chapter 4: Georeferencing of remote sensing imagery - p. 65 12. Chapter 5: Image registration - p. 77 13. Chapter 6: Mosaicking remote sensing data: techniques, challenges, and innovations - p. 101 14. Part II: Image enhancement - p. P2 15. Chapter 7: Pansharpening - p. 129 16. Chapter 8: Super-resolution of satellite imagery - p. 145 17. Chapter 9: Earth observation image denoising - p. 169 18. Part III: Image analysis - p. P3 19. Chapter 10: Semantic segmentation of Earth observation data - p. 195 20. Chapter 11: Synthesis of Earth observation imagery - p. 223 21. Chapter 12: Multimodal data fusion for semantic mapping and change detection - p. 257 22. Chapter 13: Self-supervised learning for Earth observation foundation models - p. 281 23. Chapter 14: Object detection in remote sensing - p. 315 24. Chapter 15: A tour of visual question answering for remote sensing - p. 341 25. Chapter 16: Geospatial data visualization with Python - p. 363 26. Part IV: Computing - p. P4 27. Chapter 17: Geospatial machine learning libraries - p. 401 28. Chapter 18: High-performance computing for geospatial intelligence - p. 429 29. Chapter 19: Cloud infrastructure for Earth observation imagery - p. 449 30. Index - p. 469
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. ix 7. Chapter 1: Earth observation and GeoAI through the years: six decades of progress in image analysis - p. 1 8. Part I: Image preprocessing - p. P1 9. Chapter 2: Radiometric correction - p. 9 10. Chapter 3: Rectification - p. 39 11. Chapter 4: Georeferencing of remote sensing imagery - p. 65 12. Chapter 5: Image registration - p. 77 13. Chapter 6: Mosaicking remote sensing data: techniques, challenges, and innovations - p. 101 14. Part II: Image enhancement - p. P2 15. Chapter 7: Pansharpening - p. 129 16. Chapter 8: Super-resolution of satellite imagery - p. 145 17. Chapter 9: Earth observation image denoising - p. 169 18. Part III: Image analysis - p. P3 19. Chapter 10: Semantic segmentation of Earth observation data - p. 195 20. Chapter 11: Synthesis of Earth observation imagery - p. 223 21. Chapter 12: Multimodal data fusion for semantic mapping and change detection - p. 257 22. Chapter 13: Self-supervised learning for Earth observation foundation models - p. 281 23. Chapter 14: Object detection in remote sensing - p. 315 24. Chapter 15: A tour of visual question answering for remote sensing - p. 341 25. Chapter 16: Geospatial data visualization with Python - p. 363 26. Part IV: Computing - p. P4 27. Chapter 17: Geospatial machine learning libraries - p. 401 28. Chapter 18: High-performance computing for geospatial intelligence - p. 429 29. Chapter 19: Cloud infrastructure for Earth observation imagery - p. 449 30. Index - p. 469
