Abstract
The need for remote sensing image feature selection methods is discussed in this paper. A central problem in image classification and recognition is the redundancy of image features. To cope with many unnecessary and irrelevant features, we propose a mixture method based on principle component analysis (PCA) and rough set theory to alleviate this situation. The main contribution of this paper is to provide the method for remote sensing image classification with higher accuracy comparing to the single rough set theory and PCA method. Finally, some experimental results demonstrate that our proposed method is effective in feature selection for remote sensing image.
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