Isbn: 9786139981366 - feature extraction of sar images using 2d pcm and image fusion methods (6 resultados)

Taschenbuch. Condición: Neu. Feature extraction of SAR images using 2D PCM and image fusion methods | Krishna Chaitanya Janapati (u. a.) | Taschenbuch | 184 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786139981366 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Paperback. Condición: Brand New. 184 pages. 8.66x5.91x0.42 inches. In Stock.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The ultimate objective of this book is to propose new methodologies for classification of synthetic Aperture Radar (SAR) images. There is a huge demand for classification of SAR images in military applications. In general, an analyst tries to use visual interpretation in SAR images to classify and identify the homogeneous group of elements that represents various regions like land, water, mountains and many others over a region of interest. These SAR images are different from traditional photographs and its visual interpretation is complicated. Hence there is a need to devise new strategies and methodologies for the classification of SAR images. The problem of classification can be categorized into two methods such as supervised and unsupervised. The unsupervised learning method requires optimization of metrics which often fail because most of the functions of these metrics employ local optimization methods which are non convex and irregular, so global methods are preferred to overcome this problem. 184 pp. Englisch.…

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Janapati Krishna ChaitanyaKrishna Chaithanya Janapati completed his PhD in the area of Digital Signal and Image Processing, M. TECH in the discipline of Digital Systems & Computer Electronics & B.TECH in Electronics Instrumentation &. …

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The ultimate objective of this book is to propose new methodologies for classification of synthetic Aperture Radar (SAR) images. There is a huge demand for classification of SAR images in military applications. In general, an analyst tries to use visual interpretation in SAR images to classify and identify the homogeneous group of elements that represents various regions like land, water, mountains and many others over a region of interest. These SAR images are different from traditional photographs and its visual interpretation is complicated. Hence there is a need to devise new strategies and methodologies for the classification of SAR images. The problem of classification can be categorized into two methods such as supervised and unsupervised. The unsupervised learning method requires optimization of metrics which often fail because most of the functions of these metrics employ local optimization methods which are non convex and irregular, so global methods are preferred to overcome this problem.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 184 pp. Englisch.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The ultimate objective of this book is to propose new methodologies for classification of synthetic Aperture Radar (SAR) images. There is a huge demand for classification of SAR images in military applications. In general, an analyst tries to use visual interpretation in SAR images to classify and identify the homogeneous group of elements that represents various regions like land, water, mountains and many others over a region of interest. These SAR images are different from traditional photographs and its visual interpretation is complicated. Hence there is a need to devise new strategies and methodologies for the classification of SAR images. The problem of classification can be categorized into two methods such as supervised and unsupervised. The unsupervised learning method requires optimization of metrics which often fail because most of the functions of these metrics employ local optimization methods which are non convex and irregular, so global methods are preferred to overcome this problem.…