Comparison of Processing Chains Based on Support Vector Machine Classifier for Hyperspectral Image Classification

Jee Cheng Wu; Bo Kai Lin; Gwo Chyang Tsuei

doi:10.4028/www.scientific.net/AMR.433-440.646

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 433-440Comparison of Processing Chains Based on Support...

Comparison of Processing Chains Based on Support Vector Machine Classifier for Hyperspectral Image Classification

Abstract:

In this research, we compared the performance of different processing chains resulting from combinations of spatial denoising filters, unsupervised feature transformation methods, and a support vector machine classifier. Two different training and test sample scenarios were investigated and conducted on an AVIRIS image over the Indian Pines region in Indiana, USA. The results showed that by using the process chain (adaptive enhanced Lee filter, maximum noise fraction, and support vector machine) the classification accuracies of the kappa coefficient were better than those of the best previously published techniques.

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Periodical:

Advanced Materials Research (Volumes 433-440)

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646-649

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.433-440.646

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Online since:

January 2012

Authors:

Jee Cheng Wu, Bo Kai Lin, Gwo Chyang Tsuei

Keywords:

Classification, Hyperspectral Image, Support Vector Machine (SVM)

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