Texture segmentation based on wavelet and Kohonen network for remotely sensed images

In this paper, an approach based on wavelet decomposition and Kohonen's self-organizing map is developed for image segmentation. After performing the 2D wavelet transform of image, some features are extracted for texture segmentation, and the Kohonen neural network is used to accomplish feature clustering. The experimental results demonstrated the satisfactory effect of the proposed approach both for simulated textured image and multi-spectral remotely sensed images.