Edge Fining Algorithm of Texture Classification for High Resolution Remote Sensing Images

In order to improve the classification accuracy of the high resolution remote sensing images, the algorithm based on texture features is proposed in this paper. The image is classified initially by texture features which are derived from gray level co-occurrence matrices. And the suitable weights of these features are given to form the feature vector. Secondly, the edge fining method, which can reclassify the edge pixels to proper classes, is introduced. This method adopts texture features with different parameters to reclassify the edge pixels when the edge blocks are large. And when the edge pixels blocks are small, the spectral feature is taken into consideration. Finally, two different original images are chosen to verify the algorithm. And from the results it can be seen that this algorithm is effective for high spatial resolution RS images classification.