Wavelets on graphs for very high resolution multispectral image texture segmentation

This paper proposes a texture-based segmentation method for very high spatial resolution imagery. Indeed, our main objective is to perform a sparse image representation modeled by a graph and then to exploit the wavelet transform on graph for the final purpose of image segmentation. Here, a set of pixels of interest, called representative pixels, is first extracted from the image and considered as vertices for constructing a weighted graph. Once the wavelet transform on graph is generated, their coefficients serve as textural features and will be exploited for unsupervised segmentation. Experimental results show the effectiveness of the proposed method when applied for very high spatial resolution multi-spectral images in terms of good segmentation precision as well as low complexity requirement.