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Multisensor Data Fusion and Machine - Hardcover, by Chang Ni-Bin Bai - Very Good
US $154.35
ApproximatelyEUR 138.42
Condition:
Very Good
A book that has been read and does not look new, but is in excellent condition. No obvious damage to the book cover, with the dust jacket (if applicable) included for hard covers. No missing or damaged pages, no creases or tears, no underlining or highlighting of text, and no writing in the margins. Some identifying marks on the inside cover, but this is minimal. Very little wear and tear. See the seller’s listing for full details and description of any imperfections.
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eBay item number:125457791059
Item specifics
- Condition
- Book Title
- Multisensor Data Fusion and Machine Learning for Environmental Re
- ISBN
- 9781498774338
- Subject Area
- Computers, Technology & Engineering, Science
- Publication Name
- Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing
- Publisher
- CRC Press LLC
- Item Length
- 9.8 in
- Subject
- Environmental Science (See Also Chemistry / Environmental), Remote Sensing & Geographic Information Systems, Electrical, Databases / Data Mining, Imaging Systems
- Publication Year
- 2018
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 7.1 in
- Item Weight
- 45.7 Oz
- Item Width
- 7.9 in
- Number of Pages
- 508 Pages
About this product
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
1498774334
ISBN-13
9781498774338
eBay Product ID (ePID)
240454496
Product Key Features
Number of Pages
508 Pages
Language
English
Publication Name
Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing
Publication Year
2018
Subject
Environmental Science (See Also Chemistry / Environmental), Remote Sensing & Geographic Information Systems, Electrical, Databases / Data Mining, Imaging Systems
Type
Textbook
Subject Area
Computers, Technology & Engineering, Science
Format
Hardcover
Dimensions
Item Height
7.1 in
Item Weight
45.7 Oz
Item Length
9.8 in
Item Width
7.9 in
Additional Product Features
Intended Audience
College Audience
LCCN
2017-048317
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
363.70630284
Table Of Content
Preface Acknowledgments Authors Chapter 1 Introduction Part I Fundamental Principles of Remote Sensing Chapter 2 Electromagnetic Radiation and Remote Sensing Chapter 3 Remote Sensing Sensors and Platforms Chapter 4 Image Processing Techniques in Remote Sensing Part II Feature Extraction for Remote Sensing Chapter 5 Feature Extraction and Classification for Environmental Remote Sensing Chapter 6 Feature Extraction with Statistics and Decision Science Algorithms Chapter 7 Feature Extraction with Machine Learning and Data Mining Algorithms Part III Image and Data Fusion for Remote Sensing Chapter 8 Principles and Practices of Data Fusion in Multisensor Remote Sensing for Environmental Monitoring Chapter 9 Major Techniques and Algorithms for Multisensor Data Fusion Chapter 10 System Design of Data Fusion and the Relevant Performance Evaluation Metrics Part IV Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning Chapter 11 Cross-Mission Data Merging Methods and Algorithms Chapter 12 Cloudy Pixel Removal and Image Reconstruction Chapter 13 Integrated Data Fusion and Machine Learning for Intelligent Feature Extraction Chapter 14 Integrated Cross-Mission Data Merging, Fusion, and Machine Learning Algorithms Toward Better Environmental Surveillance Part V Remote Sensing for Environmental Decision Analysis Chapter 15 Data Merging for Creating Long-Term Coherent Multisensor Chapter 16 Water Quality Monitoring in a Lake for Improving a Drinking Water Treatment Process Chapter 17 Monitoring Ecosystem Toxins in a Water Body for Sustainable Development of a Lake Watershed Chapter 18 Environmental Reconstruction of Watershed Vegetation Cover to Reflect the Impact of a Hurricane Event Chapter 19 Multisensor Data Merging and Reconstruction for Estimating PM Concentrations in a Metropolitan Region Chapter 20 Conclusions References Index
Synopsis
In the last few years the scientific community has realized that obtaining a better understanding of interactions between natural systems and the man-made environment across different scales demands more research efforts in remote sensing. An integrated Earth system observatory that merges surface-based, air-borne, space-borne, and even underground sensors with comprehensive and predictive capabilities indicates promise for revolutionizing the study of global water, energy, and carbon cycles as well as land use and land cover changes. The aim of this book is to present a suite of relevant concepts, tools, and methods of integrated multisensor data fusion and machine learning technologies to promote environmental sustainability. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The niche for integrating data fusion and machine learning for remote sensing rests upon the creation of a new scientific architecture in remote sensing science that is designed to support numerical as well as symbolic feature extraction managed by several cognitively oriented machine learning tasks at finer scales. By grouping a suite of satellites with similar nature in platform design, data merging may come to help for cloudy pixel reconstruction over the space domain or concatenation of time series images over the time domain, or even both simultaneously. Organized in 5 parts, from Fundamental Principles of Remote Sensing; Feature Extraction for Remote Sensing; Image and Data Fusion for Remote Sensing; Integrated Data Merging, Data Reconstruction, Data Fusion, and Machine Learning; to Remote Sensing for Environmental Decision Analysis , the book will be a useful reference for graduate students, academic scholars, and working professionals who are involved in the study of Earth systems and the environment for a sustainable future. The new knowledge in this book can be applied successfully in many areas of environmental science and engineering.
LC Classification Number
GE45.R44C43 2018
Item description from the seller
Business seller information
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