Selecting Key Features for Remote Sensing Classification by Using Decision-Theoretic Rough Set Model
2013
Xie, Feng | Chen, Dongmei | Meligrana, John | Lin, Yi | Ren, Wenwei
<p><i>There are many spectral bands or band functions developed for land-cover feature measurements. When the ratio of the number of training samples to the number of feature measurements is small, the traditional land-cover classification is not accurate. To solve this problem, a decision-theoretic rough set model (DTRSM) is first introduced. This model is linked with distinguishing different types of samples in the image. The samples in the minority classes will be misclassified based on the model. To minimize the misclassification, we propose an improved feature selection algorithm with comprehensive criteria. This algorithm is implemented on the Landsat TM data covering two disparate regions which are Lake Baiyangdian and Qingpu District in Shanghai located in the north and south of China, respectively. We compare the algorithm with other feature selection algorithms. Results show that the proposed method can effectively select key features for different data sets and the accuracy of classifiers can be ensured.</i></p>
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