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Advanced Image Processing Techniques for Land Feature Classification: Classification of Semi-Urban Land Use/ Land Cover Features in High Resolution RS Data - Softcover
Synopsis
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.
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About the Author
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.
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2011
- ISBN 10 3847324225
- ISBN 13 9783847324225
- BindingPaperback
- LanguageEnglish
- Number of pages260
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Advanced Image Processing Techniques for Land Feature Classification
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LAP LAMBERT Academic Publishing Dez 2011, 2011
ISBN 10: 3847324225
ISBN 13: 9783847324225
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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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
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Advanced Image Processing Techniques for Land Feature Classification : Classification of Semi-Urban Land Use/ Land Cover Features in High Resolution RS Data
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3847324225
ISBN 13: 9783847324225
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Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
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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
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Advanced Image Processing Techniques for Land Feature Classification
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3847324225
ISBN 13: 9783847324225
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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
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Advanced Image Processing Techniques for Land Feature Classification
Published by
LAP LAMBERT Academic Publishing Dez 2011, 2011
ISBN 10: 3847324225
ISBN 13: 9783847324225
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 260 pp. Englisch. Seller Inventory # 9783847324225
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Advanced Image Processing Techniques for Land Feature Classification: Classification of Semi-Urban Land Use/ Land Cover Features in High Resolution RS Data
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3847324225
ISBN 13: 9783847324225
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