Satellite Image Analysis: Clustering and Classification

Nonfiction, Science & Nature, Technology, Telecommunications, Electronics
Cover of the book Satellite Image Analysis: Clustering and Classification by Surekha Borra, Rohit Thanki, Nilanjan Dey, Springer Singapore
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Author: Surekha Borra, Rohit Thanki, Nilanjan Dey ISBN: 9789811364242
Publisher: Springer Singapore Publication: February 8, 2019
Imprint: Springer Language: English
Author: Surekha Borra, Rohit Thanki, Nilanjan Dey
ISBN: 9789811364242
Publisher: Springer Singapore
Publication: February 8, 2019
Imprint: Springer
Language: English

Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. 

This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.

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Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. 

This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.

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