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面向AI大模型:遙感影像智能解譯與應用

包郵 面向AI大模型:遙感影像智能解譯與應用

作者:馮鵬銘
出版社:哈爾濱工業大學出版社出版時間:2024-06-01
開本: 16開
本類榜單:教材銷量榜
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面向AI大模型:遙感影像智能解譯與應用 版權信息

  • ISBN:9787576715422
  • 條形碼:9787576715422 ; 978-7-5767-1542-2
  • 裝幀:平裝-膠訂
  • 冊數:暫無
  • 重量:暫無
  • 所屬分類:>

面向AI大模型:遙感影像智能解譯與應用 內容簡介

With the rapid development of deep learning technology, the information extraction methods of remotesensing images are rapidly changing from traditional statistics to data-driven intelligent interpretation. In thebackground of the rapid development of artificial intelligence technology, and supported by major projects suchas National Science and Technology Major Project of China High-resolution Earth Observation System, NationalNatural Science Foundation of China, Civil Aerospace "14th Five-Year" Technology Pre-research Project, theauthors and their team have made a series of research achievements in the filed of intelligent interpretation andapplication technology of remote sensing images. This book starts from the development status of remote sensingimage intelligent interpretation and application technology, systematically introduces the main contents of remotesensing image intelligent interpretation and application, focusing on remote sensing image intelligent qualityimprovement, intelligent expansion and sample augmentation, object detection, fine-grained target recognition,semantic segmentation, multimodal remote sensing image joint intelligent interpretation as well as intelligentinterpretation and application platform.This book can be used as a reference book for remote sensing related majors in colleges and universities,and also for research scholars and staff in the field of remote sensing. With the rapid development of deep learning technology, the information extraction methods of remotesensing images are rapidly changing from traditional statistics to data-driven intelligent interpretation. In thebackground of the rapid development of artificial intelligence technology, and supported by major projects suchas National Science and Technology Major Project of China High-resolution Earth Observation System, NationalNatural Science Foundation of China, Civil Aerospace "14th Five-Year" Technology Pre-research Project, theauthors and their team have made a series of research achievements in the filed of intelligent interpretation andapplication technology of remote sensing images. This book starts from the development status of remote sensingimage intelligent interpretation and application technology, systematically introduces the main contents of remotesensing image intelligent interpretation and application, focusing on remote sensing image intelligent qualityimprovement, intelligent expansion and sample augmentation, object detection, fine-grained target recognition,semantic segmentation, multimodal remote sensing image joint intelligent interpretation as well as intelligentinterpretation and application platform.With the rapid development of deep learning technology, the information extraction methods of remotesensing images are rapidly changing from traditional statistics to data-driven intelligent interpretation. In thebackground of the rapid development of artificial intelligence technology, and supported by major projects suchas National Science and Technology Major Project of China High-resolution Earth Observation System, NationalNatural Science Foundation of China, Civil Aerospace "14th Five-Year" Technology Pre-research Project, theauthors and their team have made a series of research achievements in the filed of intelligent interpretation andapplication technology of remote sensing images. This book starts from the development status of remote sensingimage intelligent interpretation and application technology, systematically introduces the main contents of remotesensing image intelligent interpretation and application, focusing on remote sensing image intelligent qualityimprovement, intelligent expansion and sample augmentation, object detection, fine-grained target recognition,semantic segmentation, multimodal remote sensing image joint intelligent interpretation as well as intelligentinterpretation and application platform.This book can be used as a reference book for remote sensing related majors in colleges and universities,and also for research scholars and staff in the field of remote sensing. With the rapid development of deep learning technology, the information extraction methods of remotesensing images are rapidly changing from traditional statistics to data-driven intelligent interpretation. In thebackground of the rapid development of artificial intelligence technology, and supported by major projects suchas National Science and Technology Major Project of China High-resolution Earth Observation System, NationalNatural Science Foundation of China, Civil Aerospace "14th Five-Year" Technology Pre-research Project, theauthors and their team have made a series of research achievements in the filed of intelligent interpretation andapplication technology of remote sensing images. This book starts from the development status of remote sensingimage intelligent interpretation and application technology, systematically introduces the main contents of remotesensing image intelligent interpretation and application, focusing on remote sensing image intelligent qualityimprovement, intelligent expansion and sample augmentation, object detection, fine-grained target recognition,semantic segmentation, multimodal remote sensing image joint intelligent interpretation as well as intelligentinterpretation and application platform.This book can be used as a reference book for remote sensing related majors in colleges and universities,and also for research scholars and staff in the field of remote sensing.

面向AI大模型:遙感影像智能解譯與應用 目錄

Chapter 1 Introduction1.1 Connotation1.2 Development Demand1.3 Current Development Situation1.4 Existing Problems1.5 Development Trend1.6 Chapter SummaryChapter 2 Intelligent Quality Enhancement of Remote Sensing Images2.1 Improvement of Signal-to-Noise Ratio in Remote Sensing Images2.2 Spatial Resolution Improvement of Remote Sensing Images2.3 SAt/Image Despeckling Method Based on Adversarial Learning2.4 Chapter SummaryChapter 3 Intelligent Expansion and Sample Amplification of Remote Sensing Images3.1 OverviewChapter 1 Introduction1.1 Connotation1.2 Development Demand1.3 Current Development Situation1.4 Existing Problems1.5 Development Trend1.6 Chapter SummaryChapter 2 Intelligent Quality Enhancement of Remote Sensing Images2.1 Improvement of Signal-to-Noise Ratio in Remote Sensing Images2.2 Spatial Resolution Improvement of Remote Sensing Images2.3 SAt/Image Despeckling Method Based on Adversarial Learning2.4 Chapter SummaryChapter 3 Intelligent Expansion and Sample Amplification of Remote Sensing Images3.1 Overview3.2 Intelligent Assisted Annotation3.3 Intelligent Sample Expansion3.4 Sample Self-Growth3.5 Chapter SummaryChapter 4 Intelligent Object Detection in Remote Sensing Images4.1 Overview4.2 Deep Learning Based Object Detection Framework4.3 Object Detection Datasets in Remote Sensing Images and Evaluation Metrics4.4 Target Representation Methods in Remote Sensing Images4.5 Label Assignment Strategy in Remote Sensing Images4.6 The Detection Head for Object Detection in Remote Sensing Images4.7 Loss Function for Object Detection4.8 Chapter SummaryChapter 5 Fine-Grained Target Recognition of Remote Sensing Image5.1 Overview5.2 Challenges of Fine-Grained Recognition5.3 Few-Shot Target Recognition of Multi-Scale Network5.4 Classification of Highly Imbalanced Aviation Scenes5.6 Chapter SummaryChapter 6 Intelligent Semantic Segmentation6.1 Overview6.2 Design of Semantic Segmentation Model Based on Deep Learning6.4 Chapter SummaryChapter 7 Joint Intelligent Interpretation of Multimodal Remote Sensing Images7.1 Overview7.2 Muhimodal Remote Sensing Dataset Construction7.3 Heterogeneous Remote Sensing Image Registration7.4 Fusion and Classification of Multimodal Data7.5 Chapter SummaryChapter 8 Intelligent Remote Sensing Image Analysis: A Cognitive Platform for Interpretation8.1 Overview8.2 Platform Architecture8.3 Platform Introduction8.4 Chapter SummaryReferences
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