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20 October 2015 Fault tolerance of SVM algorithm for hyperspectral image
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Abstract
One of the most important tasks in analyzing hyperspectral image data is the classification process[1]. In general, in order to enhance the classification accuracy, a data preprocessing step is usually adopted to remove the noise in the data before classification. But for the time-sensitive applications, we hope that even the data contains noise the classifier can still appear to execute correctly from the user’s perspective, such as risk prevention and response. As the most popular classifier, Support Vector Machine (SVM) has been widely used for hyperspectral image classification and proved to be a very promising technique in supervised classification[2]. In this paper, two experiments are performed to demonstrate that for the hyperspectral data with noise, if the noise of the data is within a certain range, SVM algorithm is still able to execute correctly from the user’s perspective.
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Yabo Cui, Zhengwu Yuan, Yuanfeng Wu, Lianru Gao, and Hao Zhang "Fault tolerance of SVM algorithm for hyperspectral image", Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 964610 (20 October 2015); https://doi.org/10.1117/12.2196704
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