Nagarajan Sureshkumar (7 results)

Paperback. Condition: Brand New. 01 edition. 112 pages. 8.66x5.91x0.26 inches. In Stock.

Taschenbuch. Condition: Neu. Accuracy Analysis of Satellite Image Classification Techniques | Land Cover Changes using Combined LANDSAT and ENVISAT Images | Nagarajan Sureshkumar (u. a.) | Taschenbuch | 112 S. | Englisch | 2016 | LAP Lambert Academic Publishing | EAN 9783330013827 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Recent monsoon failures and reduced rain falls urge the environmental and ecology researchers to concentrate on the land cover changes. Significant and efficient way to monitor the land cover changes is satellite image classification. Classification of land cover changes of the study area are identified as used land, unused land, forest and vegetation. Using different kinds of remote sensing data like LANDSAT and ENVISAT, is an important research area for improving the classification performance. This work describes the combination of remotely sensed data, LANDSAT and ENVISAT images, to improve the classification accuracy. Classification algorithms KNN (K-Nearest Neighborhood) and SVM (Support Vector Machine) are tested for the accuracy and KNN in Embedding Space (KNNES) and SVM in Embedding Space (SVMES) are proposed and tested for the improved accuracy. Accuracy is quantified by reporting standard errors i.e., producer accuracy, user accuracy, omission error and commission error. 112 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sureshkumar NagarajanSureshkumar N obtained PhD from VIT University. Arun M received PhD from Anna University, Chennai and Post-Doctoral Fellow at University of Aveiro, Portugal. Authors are professors at School of Computing Science .

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Recent monsoon failures and reduced rain falls urge the environmental and ecology researchers to concentrate on the land cover changes. Significant and efficient way to monitor the land cover changes is satellite image classification. Classification of land cover changes of the study area are identified as used land, unused land, forest and vegetation. Using different kinds of remote sensing data like LANDSAT and ENVISAT, is an important research area for improving the classification performance. This work describes the combination of remotely sensed data, LANDSAT and ENVISAT images, to improve the classification accuracy. Classification algorithms KNN (K-Nearest Neighborhood) and SVM (Support Vector Machine) are tested for the accuracy and KNN in Embedding Space (KNNES) and SVM in Embedding Space (SVMES) are proposed and tested for the improved accuracy. Accuracy is quantified by reporting standard errors i.e., producer accuracy, user accuracy, omission error and commission error.Books on Demand GmbH, Überseering 33, 22297 Hamburg 112 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Recent monsoon failures and reduced rain falls urge the environmental and ecology researchers to concentrate on the land cover changes. Significant and efficient way to monitor the land cover changes is satellite image classification. Classification of land cover changes of the study area are identified as used land, unused land, forest and vegetation. Using different kinds of remote sensing data like LANDSAT and ENVISAT, is an important research area for improving the classification performance. This work describes the combination of remotely sensed data, LANDSAT and ENVISAT images, to improve the classification accuracy. Classification algorithms KNN (K-Nearest Neighborhood) and SVM (Support Vector Machine) are tested for the accuracy and KNN in Embedding Space (KNNES) and SVM in Embedding Space (SVMES) are proposed and tested for the improved accuracy. Accuracy is quantified by reporting standard errors i.e., producer accuracy, user accuracy, omission error and commission error.