9786139848898 - Land Use Land Cover Change Detection: Fuzzy Approach von J, Jayanth (8 Ergebnisse)

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Taschenbuch. Zustand: Neu. Land Use Land Cover Change Detection: Fuzzy Approach | Jayanth J | Taschenbuch | 116 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139848898 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Change detection approach is capable of mapping the proportional land cover of each class for every pixel. Fuzzy image classification is useful, if not indispensable, for advanced change detection techniques considering processes and therefore the intensity of land cover change. Clear land cover changes from one class to the other. The Overall Classification Accuracy obtained is 77.34%, which can be further improved. As the successful change estimation in our work mainly depends on the classification of our data sets with high accuracies linearly. Also our study does not include knowledge base. If knowledge base is incorporated into the system, the accuracy of classification would improve. As a post classification procedure for change detection, in the place of Fuzzy classification, Maximum likelihood classifiers can also be used. Cross-Correlation Analysis (CCA) can also be used to identify changes that have occurred in a previously mapped area. The procedure uses a recent multi-spectral image with a thematic land cover map in a two-step process. 116 pp. Englisch.

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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: J JayanthJayanth J received the B.E. degree in Electronics and Communication Engineering from Vidya Vikas Institute of Engineering and Technology, Mysore, Karnataka, India in the year 2008, and M.Tech. in Digital Electronics and Comm.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Change detection approach is capable of mapping the proportional land cover of each class for every pixel. Fuzzy image classification is useful, if not indispensable, for advanced change detection techniques considering processes and therefore the intensity of land cover change. Clear land cover changes from one class to the other. The Overall Classification Accuracy obtained is 77.34%, which can be further improved. As the successful change estimation in our work mainly depends on the classification of our data sets with high accuracies linearly. Also our study does not include knowledge base. If knowledge base is incorporated into the system, the accuracy of classification would improve. As a post classification procedure for change detection, in the place of Fuzzy classification, Maximum likelihood classifiers can also be used. Cross-Correlation Analysis (CCA) can also be used to identify changes that have occurred in a previously mapped area. The procedure uses a recent multi-spectral image with a thematic land cover map in a two-step process.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Change detection approach is capable of mapping the proportional land cover of each class for every pixel. Fuzzy image classification is useful, if not indispensable, for advanced change detection techniques considering processes and therefore the intensity of land cover change. Clear land cover changes from one class to the other. The Overall Classification Accuracy obtained is 77.34%, which can be further improved. As the successful change estimation in our work mainly depends on the classification of our data sets with high accuracies linearly. Also our study does not include knowledge base. If knowledge base is incorporated into the system, the accuracy of classification would improve. As a post classification procedure for change detection, in the place of Fuzzy classification, Maximum likelihood classifiers can also be used. Cross-Correlation Analysis (CCA) can also be used to identify changes that have occurred in a previously mapped area. The procedure uses a recent multi-spectral image with a thematic land cover map in a two-step process.