An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing |
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Authors: | Thomas Oommen Debasmita Misra Navin K C Twarakavi Anupma Prakash Bhaskar Sahoo Sukumar Bandopadhyay |
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Institution: | (1) Department of Civil and Environmental Engineering, Tufts University, 200 College Avenue, Medford, MA 02155, USA;(2) Department of Mining and Geological Engineering, University of Alaska-Fairbanks, P.O. Box 755800, Fairbanks, AK 99775, USA;(3) Department of Environmental Sciences, University of California-Riverside, Riverside, CA 92521, USA;(4) Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Dr, Fairbanks, AK 99775-7320, USA |
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Abstract: | Accurate thematic classification is one of the most commonly desired outputs from remote sensing images. Recent research efforts
to improve the reliability and accuracy of image classification have led to the introduction of the Support Vector Classification
(SVC) scheme. SVC is a new generation of supervised learning method based on the principle of statistical learning theory,
which is designed to decrease uncertainty in the model structure and the fitness of data. We have presented a comparative
analysis of SVC with the Maximum Likelihood Classification (MLC) method, which is the most popular conventional supervised
classification technique. SVC is an optimization technique in which the classification accuracy heavily relies on identifying
the optimal parameters. Using a case study, we verify a method to obtain these optimal parameters such that SVC can be applied
efficiently. We use multispectral and hyperspectral images to develop thematic classes of known lithologic units in order
to compare the classification accuracy of both the methods. We have varied the training to testing data proportions to assess
the relative robustness and the optimal training sample requirement of both the methods to achieve comparable levels of accuracy.
The results of our study illustrated that SVC improved the classification accuracy, was robust and did not suffer from dimensionality
issues such as the Hughes Effect. |
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Keywords: | Remote sensing Support vector machines Maximum likelihood Multispectral Hyperspectral classification |
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