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Satellite Image Analysis: Clustering and Classification [electronic resource] / by Surekha Borra, Rohit Thanki, Nilanjan Dey.

By: Borra, Surekha [author.].
Contributor(s): Thanki, Rohit [author.] | Dey, Nilanjan [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Computational Intelligence: Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2019Edition: 1st ed. 2019.Description: XVI, 97 p. 53 illus., 22 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789811364242.Subject(s): Signal processing | Telecommunication | Computer vision | Signal, Speech and Image Processing | Communications Engineering, Networks | Computer VisionAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 621.382 Online resources: Click here to access online
Contents:
Preface -- Introduction -- Image Pre-processing Techniques -- Satellite Image Clustering -- Satellite Image Classification -- Applied Examples -- Conclusion.
In: Springer Nature eBookSummary: 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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Preface -- Introduction -- Image Pre-processing Techniques -- Satellite Image Clustering -- Satellite Image Classification -- Applied Examples -- Conclusion.

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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