Selecting Key Features for Remote Sensing Classification by Using Decision-Theoretic Rough Set Model
Feng Xie, Dongmei Chen, John F. Meligrana, Yi Lin, Wenwei Ren · Photogrammetric Engineering & Remote Sensing · 2013
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 classifi cation is not accurate. To solve this problem, a decision-theoretic rough set model (DTRSM) is fi rst introduced. This model is linked with distinguishing different types of samples in the image. The samples in the minority classes will be misclassifi ed based on the model. To minimize the misclassifi cation, 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 Lake Qingpu 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 classifi ers can be ensured.