2 July 2015 Edge-pixels-based support vector data description for specific land-cover distribution mapping
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Abstract
An edge-pixels-based support vector data description (EPSVDD) method has been developed for improving one-class classification accuracy. The proposed method was validated in two experiments: a simulated experiment and an actual experiment. In the simulated experiment, a ring segmentation search method was performed to segment the wheat spectral feature for deriving training samples of different spectral responses. As the training data moved from the center to the edge of wheat distribution, the hypersphere expanded and the overall accuracy (OA) simultaneously increased, highlighting the potential advantage of edge pixels in SVDD classification. In the actual experiment, edge training samples were manually acquired from geographical parcel boundary and minimum noise fraction (MNF) scatterplots for both wheat and bare-land classes. For the wheat class, EPSVDD yielded an improved classification with an OA of 92.71% and a producer’s accuracy of 95.81%, which were higher than those of conventional SVDD method using typical training samples. Similarly, for the bare-land class, the OA of the EPSVDD was 92.53%, which was also significantly higher than traditional SVDD method. Then, SVDD classifications were carried out and repeated 10 times using different training set sizes. Mean OAs were almost higher than 0.9 with variance less than 0.03 using edge training samples, while highest OAs for wheat and bare land classes were 0.74 and 0.81, respectively, using random sampling method. The EPSVDD can effectively select the informative training sample for SVDD classifier to improve the accuracy of one-class classification.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Guanyuan Shuai, Guanyuan Shuai, Jinshui Zhang, Jinshui Zhang, Lei Deng, Lei Deng, Xiufang Zhu, Xiufang Zhu, } "Edge-pixels-based support vector data description for specific land-cover distribution mapping," Journal of Applied Remote Sensing 9(1), 096034 (2 July 2015). https://doi.org/10.1117/1.JRS.9.096034 . Submission:
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