Compressed texton based sorted visual words co-occurrence matrix for high resolution remote sensing imagery classifcation

A novel, simple, yet effective texture extraction method for high resolution remote sensing imagery classification based on visual words co-occurrence matrix is proposed in this paper. First, Local texture is represented by compressed texton learned from raw image patch with a sorting scheme and random projection. Then the sorted visual words co-occurrence matrix obtained with dictionary learning and nearest neighbor encoding is used for representing global texture. Finally, the support vector machine is applied for classification. Two imagery from Pavia city of Italy with public ground truth dataset are used in our experiments. The results show that the proposed method is effective and outperforms other existing methods.