Since the traditional hard classifiers are parametric in nature and expect the data to follow a Gaussian distribution, they perform poorly on high resolution satellite images in which land features and classes exhibit extensive overlapping in spectral space. Further, integrating ancillary data like digital elevation model, slope, texture, contextual information, etc. into spectral bands is difficult in such classifiers, because ancillary data results in a non-Gaussian distribution of the resultant data. Hence, generating a satisfactory classified image from the higher spectral and spatial, and high-dimensional data is one of the present-day challenges in RS data analysis. This thesis is aimed at developing an advanced classification strategy by integrating a non-parametric J4.8 decision tree classification algorithm and a texture based image classification approach on a panchromatic sharpened IRS P-6 LISS-IV (2.5m) imagery. Attempt has also been made to provide answers through empirical studies to some of the dubious issues and contradictory findings in RS image classification with regard to image evaluation metrics, statistical feature selection criteria and border-effect.
"synopsis" may belong to another edition of this title.
Dr. Ashok Kumar received the BE and ME degree in Electronics and Commn. and Ph.D by VTU, India, for his work on Advanced Image Processing Techniques and Algorithms for Classification of High Resolution RS Data. His subjects of interest are Image Processing, Communication Engg., Data Mining and Remote Sensing. He is in teaching for over 22 years.
"About this title" may belong to another edition of this title.
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Since the traditional hard classifiers are parametric in nature and expect the data to follow a Gaussian distribution, they perform poorly on high resolution satellite images in which land features and classes exhibit extensive overlapping in spectral space. Further, integrating ancillary data like digital elevation model, slope, texture, contextual information, etc. into spectral bands is difficult in such classifiers, because ancillary data results in a non-Gaussian distribution of the resultant data. Hence, generating a satisfactory classified image from the higher spectral and spatial, and high-dimensional data is one of the present-day challenges in RS data analysis. This thesis is aimed at developing an advanced classification strategy by integrating a non-parametric J4.8 decision tree classification algorithm and a texture based image classification approach on a panchromatic sharpened IRS P-6 LISS-IV (2.5m) imagery. Attempt has also been made to provide answers through empirical studies to some of the dubious issues and contradictory findings in RS image classification with regard to image evaluation metrics, statistical feature selection criteria and border-effect. 260 pp. Englisch. Seller Inventory # 9783847324225
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar T. AshokDr. Ashok Kumar received the BE and ME degree in Electronics and Commn. and Ph.D by VTU, India, for his work on Advanced Image Processing Techniques and Algorithms for Classification of High Resolution RS Data. His subj. Seller Inventory # 5510111
Quantity: Over 20 available
Seller: preigu, Osnabrück, Germany
Taschenbuch. Condition: Neu. Advanced Image Processing Techniques for Land Feature Classification | Classification of Semi-Urban Land Use/ Land Cover Features in High Resolution RS Data | Ashok Kumar T. | Taschenbuch | 260 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783847324225 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 106676659
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Since the traditional hard classifiers are parametric in nature and expect the data to follow a Gaussian distribution, they perform poorly on high resolution satellite images in which land features and classes exhibit extensive overlapping in spectral space. Further, integrating ancillary data like digital elevation model, slope, texture, contextual information, etc. into spectral bands is difficult in such classifiers, because ancillary data results in a non-Gaussian distribution of the resultant data. Hence, generating a satisfactory classified image from the higher spectral and spatial, and high-dimensional data is one of the present-day challenges in RS data analysis. This thesis is aimed at developing an advanced classification strategy by integrating a non-parametric J4.8 decision tree classification algorithm and a texture based image classification approach on a panchromatic sharpened IRS P-6 LISS-IV (2.5m) imagery. Attempt has also been made to provide answers through empirical studies to some of the dubious issues and contradictory findings in RS image classification with regard to image evaluation metrics, statistical feature selection criteria and border-effect.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 260 pp. Englisch. Seller Inventory # 9783847324225
Seller: AHA-BUCH GmbH, Einbeck, Germany
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Since the traditional hard classifiers are parametric in nature and expect the data to follow a Gaussian distribution, they perform poorly on high resolution satellite images in which land features and classes exhibit extensive overlapping in spectral space. Further, integrating ancillary data like digital elevation model, slope, texture, contextual information, etc. into spectral bands is difficult in such classifiers, because ancillary data results in a non-Gaussian distribution of the resultant data. Hence, generating a satisfactory classified image from the higher spectral and spatial, and high-dimensional data is one of the present-day challenges in RS data analysis. This thesis is aimed at developing an advanced classification strategy by integrating a non-parametric J4.8 decision tree classification algorithm and a texture based image classification approach on a panchromatic sharpened IRS P-6 LISS-IV (2.5m) imagery. Attempt has also been made to provide answers through empirical studies to some of the dubious issues and contradictory findings in RS image classification with regard to image evaluation metrics, statistical feature selection criteria and border-effect. Seller Inventory # 9783847324225
Seller: Mispah books, Redhill, SURRE, United Kingdom
Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book. Seller Inventory # ERICA78738473242256
Quantity: 1 available