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Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Chang, Ni-Bin. Bai, Kaixu. |
| 서명/저자사항 | Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing. |
| 발행사항 | Milton : Taylor and Francis, 2018. |
| 형태사항 | 1 online resource (529 pages) |
| 소장본 주기 | Master record variable field(s) change: 050, 072, 082, 650 - OCLC control number change |
| ISBN | 9781351650632 1351650637 9781498774345 1498774342 9781315154602 1315154609 |
| 일반주기 |
6.2.2 Mathematical Morphology.
|
| 서지주기 | Includes bibliographical references. |
| 내용주기 | Cover; Half Title; Title Page; Copyright Page; Contents; Preface; Acknowledgments; Authors; Chapter 1: Introduction; 1.1 Background; 1.2 Objectives and Definitions; 1.3 Featured Areas of the Book; References; Part I: Fundamental Principles of Remote Sensing; Chapter 2: Electromagnetic Radiation and Remote Sensing; 2.1 Introduction; 2.2 Properties of Electromagnetic Radiation; 2.3 Solar Radiation; 2.4 Atmospheric Radiative Transfer; 2.4.1 Principles of Radiative Transfer; 2.4.2 Reflection; 2.4.3 Refraction; 2.5 Remote Sensing Data Collection; 2.5.1 Atmospheric Windows for Remote Sensing. 2.5.2 Specific Spectral Region for Remote Sensing2.5.3 Band Distribution for Remote Sensing; 2.6 Rationale of Thermal Remote Sensing; 2.6.1 Thermal Radiation; 2.6.2 Energy Budget and Eartha?#x80;#x99;s Net Radiation; 2.7 Basic Terminologies of Remote Sensing; 2.8 Summary; References; Chapter 3: Remote Sensing Sensors and Platforms; 3.1 Introduction; 3.2 Remote Sensing Platforms; 3.2.1 Space-Borne Platforms; 3.2.2 Air-Borne Platforms; 3.2.3 Ground- or Sea-Based Platforms; 3.3 Remote Sensing Sensors; 3.3.1 Passive Sensors; 3.3.2 Active Sensors; 3.4 Real-World Remote Sensing Systems. 3.5 Current, Historical, and Future Important Missions3.5.1 Current Important Missions; 3.5.2 Historic Important Missions; 3.5.3 Future Important Missions; 3.6 System Planning of Remote Sensing Applications; 3.7 Summary; References; Chapter 4: Image Processing Techniques in Remote Sensing; 4.1 Introduction; 4.2 Image Processing Techniques; 4.2.1 Pre-Processing Techniques; 4.2.1.1 Atmospheric Correction; 4.2.1.2 Radiometric Correction; 4.2.1.3 Geometric Correction; 4.2.1.4 Geometric Transformation; 4.2.1.5 Resampling; 4.2.1.6 Mosaicking; 4.2.1.7 Gap Filling. 4.2.2 Advanced Processing Techniques4.2.2.1 Image Enhancement; 4.2.2.2 Image Restoration; 4.2.2.3 Image Transformation; 4.2.2.4 Image Segmentation; 4.3 Common Software for Image Processing; 4.3.1 ENVI; 4.3.2 ERDAS IMAGINE; 4.3.3 PCI Geomatica; 4.3.4 ArcGIS; 4.3.5 MATLABA?짰; 4.3.6 IDL; 4.4 Summary; References; Part II: Feature Extraction for Remote Sensing; Chapter 5: Feature Extraction and Classification for Environmental Remote Sensing; 5.1 Introduction; 5.2 Feature Extraction Concepts and Fundamentals; 5.2.1 Definition of Feature Extraction; 5.2.2 Feature and Feature Class. 5.2.3 Fundamentals of Feature Extraction5.3 Feature Extraction Techniques; 5.3.1 Spectral-Based Feature Extraction; 5.3.2 Spatial-Based Feature Extraction; 5.4 Supervised Feature Extraction; 5.5 Unsupervised Feature Extraction; 5.6 Semi-supervised Feature Extraction; 5.7 Image Classification Techniques with Learning Algorithms; 5.8 Performance Evaluation Metric; 5.9 Summary; References; Chapter 6: Feature Extraction with Statistics and Decision Science Algorithms; 6.1 Introduction; 6.2 Statistics and Decision Science-Based Feature Extraction Techniques; 6.2.1 Filtering Operation. |
| 이용제한사항 | Owing to Legal Deposit regulations this resource may only be accessed from within National Library of Scotland on library computers. For more information contact enquiries@nls.uk. |
| 요약 | "Automated image fusion processes involving cross-mission of multiple satellites with the aid of ground-based sensor networks and databases are critical to support environmental decision-making. This book is unique because it rests upon a smooth integration between image fusion and data mining for information retrieval and content-based mapping in the context of different environmental applications, and it focuses on environmental application issues at global and regional scale, while using local scale ground-truth data for calibration and validation. It has potential to be integrated with local scale data/image fusion based on local physical sensors and human observations."--Provided by publisher. |
| 일반주제명 | Environmental sciences -- Remote sensing. Environmental monitoring -- Remote sensing. Content-based image retrieval. Multisensor data fusion. Multimedia data mining. BUSINESS & ECONOMICS / Infrastructure. SOCIAL SCIENCE / General. Content-based image retrieval. Environmental monitoring -- Remote sensing. Environmental sciences -- Remote sensing. Multimedia data mining. Multisensor data fusion. |
| 언어 | 영어 |
| 기타형태 저록 | Print version:Chang, Ni-Bin.Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing.Milton : Taylor and Francis, 짤20189781498774338 |
| 대출바로가기 | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1729155 |
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