Paul Subir (13 results)

Condition: New. In English.

Paperback. Condition: Brand New. 216 pages. 9.00x6.00x0.54 inches. In Stock.

Paperback. Condition: Brand New. 243 pages. 9.00x6.00x0.61 inches. In Stock.

Paperback. Condition: Brand New. In Stock.

Paperback. Condition: Brand New. 358 pages. 9.25x7.50x0.90 inches. In Stock.

Condition: New.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In recent years, remote sensing images have great potential for continuous spatial and temporal monitoring of Earth surface features. High-dimensional hyperspectral (HS) data are highly resourceful compared with multispectral (MS) data but handling such large volume data is a very challenging task, which should be addressed with the use of feature selection or feature extraction-based dimensionality reduction techniques. The major focus of this book is to demonstrate recently proposed computationally efficient approaches based on advanced machine learning and deep learning techniques to achieve better performance for land cover classification, MS to HS data transformation, and chlorophyll content prediction. The research works (i.e. developed techniques and end products), presented in this book, have the potential applications in hydrological modeling, irrigation water management, vegetation condition monitoring, crop yield forecasting, crop insurance planning, etc. In this era, when different space agencies of several countries are planning to launch HS satellite, these research works prove the importance of HS data for numerous applications. 188 pp. Englisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Paul SubirDr. Subir Paul is working as a Research Associate at the Interdisciplinary Center for Water Research and D. Nagesh Kumar is Professor in the Department of Civil Engineering of Indian Institute of Science, Bengaluru, India. .…

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Taschenbuch. Condition: Neu. Hyperspectral Remote Sensing for Land Cover Classification | Hyperspectral Data for Land Cover Classification and Chlorophyll Content Estimation Using Machine Learning Techniques | Subir Paul (u. a.) | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786202794930 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In recent years, remote sensing images have great potential for continuous spatial and temporal monitoring of Earth surface features. High-dimensional hyperspectral (HS) data are highly resourceful compared with multispectral (MS) data but handling such large volume data is a very challenging task, which should be addressed with the use of feature selection or feature extraction-based dimensionality reduction techniques. The major focus of this book is to demonstrate recently proposed computationally efficient approaches based on advanced machine learning and deep learning techniques to achieve better performance for land cover classification, MS to HS data transformation, and chlorophyll content prediction. The research works (i.e. developed techniques and end products), presented in this book, have the potential applications in hydrological modeling, irrigation water management, vegetation condition monitoring, crop yield forecasting, crop insurance planning, etc. In this era, when different space agencies of several countries are planning to launch HS satellite, these research works prove the importance of HS data for numerous applications.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 188 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In recent years, remote sensing images have great potential for continuous spatial and temporal monitoring of Earth surface features. High-dimensional hyperspectral (HS) data are highly resourceful compared with multispectral (MS) data but handling such large volume data is a very challenging task, which should be addressed with the use of feature selection or feature extraction-based dimensionality reduction techniques. The major focus of this book is to demonstrate recently proposed computationally efficient approaches based on advanced machine learning and deep learning techniques to achieve better performance for land cover classification, MS to HS data transformation, and chlorophyll content prediction. The research works (i.e. developed techniques and end products), presented in this book, have the potential applications in hydrological modeling, irrigation water management, vegetation condition monitoring, crop yield forecasting, crop insurance planning, etc. In this era, when different space agencies of several countries are planning to launch HS satellite, these research works prove the importance of HS data for numerous applications.…