Towards AI Large Model: Remote Sensing Image Intelligent Interpretation and Application
Description
This book starts from the development status of remote sensing image intelligent interpretation and application technology. It systematically introduces the main progress of this field and its application, focusing on remote sensing image intelligent quality improvement, intelligent expansion and sample augmentation, object detection, fine-grained target recognition, semantic segmentation, multimodal remote sensing image joint intelligent interpretation as well as intelligent interpretation and application platform. With the rapid development of large models and artificial intelligence technology, and supported by major projects such as the National Science and Technology Major Project of China High-resolution Earth Observation System, the authors and their team have made a series of research achievements in the field of intelligent interpretation and application technology of remote sensing images.
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Pengming Feng, Ph.D., senior engineer, master's supervisor, researcher at the National Key Laboratory of Space-Ground Integrated Information Technology at the 503rd Institute of the China Academy of Space Technology. He has been deeply involved in the research of satellite remote sensing intelligent interpretation technology and has successively led more than 10 pre-research and modeling projects, including the National Natural Science Foundation. He has published more than 30 papers and obtained more than 20 patents.
Yuanwei Chen , Master's degree holder, researcher, system designer at Aerospace Xingxing Technology Co., Ltd. He has been deeply involved in research on satellite ground system and application overall technology, and has been responsible for delivering more than 10 engineering models at home and abroad. He has published more than 10 papers.
Haiyan Lan , Ph.D., lecturer, master's supervisor, is a faculty member of the School of Computer Science and Technology, Harbin Engineering University. She has been engaged in research on signal processing and deep learning methods, and has published over 20 academic papers and obtained over 10 patents and software copyrights. She has also won the Second Prize in the Natural Science and Technological Academic Achievement of Heilongjiang Province.
Yang Li, Master's degree holder, engineer, is a system designer at Aerospace Xingxing Technology Co., Ltd. He has been deeply involved in the research of overall technical solutions for remote sensing satellite ground systems and has participated in completing more than 8 engineering models. He has published more than 8 academic papers and patents.
Jian Guan , Ph.D., associate professor, master's supervisor, is a faculty member of the School of Computer Science and Technology, Harbin Engineering University. He has been engaged in research on target detection/recognition methods in remote sensing and deep learning theory and applications for a long time. He has published more than 50 academic papers and applied for and obtained over 10 invention patents.
Guangjun He , Ph.D., senior engineer, master's supervisor, is the head of the Space Information Fusion Research Room at the National Key Laboratory of Space Integrated Information Technology at the 5th Research Institute of China Academy of Space Technology (CAST), where he has been engaged in research on satellite remote sensing application technology for a long time. He has hosted over 20 national natural science foundation projects and published more than 40 academic papers. He has also been granted over 30 patents.
Content
Introduction.- Intelligent Quality Enhancement of Remote Sensing Images.- Intelligent Expansion and Sample Augmentation for Remote Sensing Images.- Intelligent Object Detection in Remote Sensing Images.- Intelligent Fine-Grained Object Recognition in Remote Sensing Images.- Intelligent Semantic Segmentation of Remote Sensing Images.- Multimodal Remote Sensing Image Joint Intelligent Interpretation.- Remote Sensing Image Intelligent Interpretation and Application Platform.