A Hierarchical Hybrid SVM Method for Classification of Remotely Sensed Data |
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Authors: | T Ch Malleswara Rao G Jai Sankar T Roopesh Kumar |
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Institution: | 1.App. Software & Facility,NRSC, ISRO, DOS,Hyderabad,India;2.VBIT,Ghatkesar,India;3.Geo-Engineering & Resource Development Technology, College of Engineering,Andhra University,Visakhapatnam,India;4.Secunderabad,India |
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Abstract: | The focus of this work is on developing a new hierarchical hybrid Support Vector Machine (SVM) method to address the problems
of classification of multi or hyper spectral remotely sensed images and provide a working technique that increases the classification
accuracy while lowering the computational cost and complexity of the process. The paper presents issues in analyzing large
multi/hyper spectral image data sets for dimensionality reduction, coping with intra pixel spectral variations, and selection
of a flexible classifier with robust learning process. Experiments conducted revealed that a computationally cheap algorithm
that uses Hamming distance between the pixel vectors of different bands to eliminate redundant bands was quite effective in
helping reduce the dimensionality. The paper also presents the concept of extended mathematical morphological profiles for
segregating the input pixel vectors into pure or mixed categories which will enable further computational cost reductions.
The proposed method’s overall classification accuracy is tested with IRS data sets and the Airborne Visible Infrared Imaging
Spectroradiometer Indian Pines hyperspectral benchmark data set and presented. |
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Keywords: | |
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