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E-grāmata: Signal and Image Processing for Remote Sensing

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Edited by (University of Massachusetts, Dartmouth, USA)
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Advances in signal and image processing for remote sensing have been tremendous in recent years. The progress has been particularly evident with the use of machine learning, especially deep learning, approaches for remote sensing problems. These advancements are the focus of this third edition of Signal and Image Processing for Remote Sensing. It emphasizes the use of machine learning approaches for the extraction of remote sensing information. Other topics include change detection in remote sensing and compressed sensing. With 19 new chapters written by world leaders in the field, this book provides an authoritative examination and offers a unique point of view on signal and image processing.

Features

  • Includes all new content and does not replace the previous edition.
  • Covers machine learning approaches in both signal and image processing for remote sensing.
  • Studies deep learning methods for remote sensing information extraction, not found in other books.
  • Explains SAR, microwave, seismic, GPR, and hyperspectral sensors and all sensors considered.
  • Discusses improved pattern classification approaches and compressed sensing approaches.
  • Provides ample examples of each aspect of both signal and image processing.

This book is intended for university academics, researchers, postgraduate students, industry, and government professionals who use remote sensing and its applications.



This new edition of Signal and Image Processing for Remote Sensing emphasizes the use of machine learning approaches to remote sensing information extraction, change detection, and compressed sensing. With 19 new chapters written by world leaders in the field, it provides an authoritative coverage of the recent progress in the field.

Part I: General Topics.
1. An Overview of 60 Years of Progress on Signal/Image Processing for Remote Sensing.
2. Proven Approaches of Using Innovative High-Performance Computing Architectures in Remote Sensing. Part II: Signal Processing for Remote Sensing.
3. Machine Learning Techniques for Geophysical Parameter Retrievals.
4. Subsurface Inverse-Profiling and Imaging Using Stochastic Optimization Techniques.
5. Close and Remote GPR Surveys via Microwave Tomography, State of Art and Perspectives.
6. Polarimetric SAR Signatures of Complex Scene - A Simulation Study.
7. Machine Learning for Arctic Sea Physical Properties Estimation Using Dual-Polarimetric SAR Data.
8. Riemannian Clustering of Polarimetric SAR Data Using the Polar Decomposition.
9. Seismic Velocity Picking Using Hopfield Neural Network.
10. Expanded Radial Basis Function Network with Proof of Hidden Node Number by Recurrence Relation for Well Log Data Inversion. Part III: Image Processing for Remote Sensing.
11. Convolutional Neural Networks Meet Markov Random Fields for Semantic Segmentation of Remote Sensing Images.
12. Deep Learning Methods for Satellite Image Super Resolution.
13. Machine learning in Remote Sensing.
14. Robust Training of Deep Neural Networks with Weak Labeled Data.
15. Sementic Segmentation with otbtf and keras.
16. Performance of a Diffusion Model for Instance Segmentation in Remote Sensing Imagery.
17. Land Cover Classification Using Attention Based Multi Model Image Fusion: An Explainable Analysis
18. FPGA Compressive Sensing Method Applied to Hyperspectral Imagery.
19. Large-Scale Fine-Grained Change Detection from Multisensory Satellite Images.
20. Change Detection on Graphs: Exploiting Graph Structure from Bi-temporal Satellite Imagery.
21. Target Detection in Hyperspectral Imaging Using Neural Networks.

Prof. C.H. Chen received his Ph. D in electrical engineering from Purdue University West Lafayette, Indiana, in 1965, his MSEE from the University of Tennessee, Knoxville, in 1962, and his BSEE from the National Taiwan University, Taipei in 1959. He is currently the chancellor professor emeritus of electrical and computer engineering at the University of Massachusetts Dartmouth, where he has been a faculty member since 1968. His research areas encompass statistical pattern recognition and signal/image processing with applications to remote sensing, medical imaging, geophysical, underwater acoustics, and nondestructive testing problems, as well as computer vision for video surveillance, time-series analysis, and neural networks. He has edited and authored 37 books in his areas of research, including Digital Waveform Processing and Recognition (CRC Press 1982), Signal and Image Processing for Remote Sensing (CRC Press, first edition 2006, second edition 2012), and Compressive Sensing of Earth Observations (CRC Press 2017). He served as associate editor of the IEEE Transactions on Acoustic, Speech, and Signal Processing for 4 years, associate editor of the IEEE Transactions on Geoscience and Remote Sensing for 15 years, and since 2008 he has been a board member/associate editor of Pattern Recognition particularly on remote sensing topics. Dr. Chen has been a Fellow of the Institute of Electrical and Electronic Engineers (IEEE) since 1988, a Life Fellow of the IEEE since 2003, and a Fellow of the International Association of Pattern Recognition (IAPR) since 1996. He is also the editor of the book series entitled Signal and Image Processing of Earth Observations, for CRC Press.
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