Pointwise Graph-Based Local Texture Characterization for Very High Resolution Multispectral Image Classification

A new method for local texture characterization in very high resolution (VHR) multispectral imagery is proposed based on a pointwise approach embedded into a graph model. Due to the fact that increasing the spatial resolution of satellite sensors leads to the lack of stationarity hypothesis in optical images, a pointwise approach based on a set of interest pixels only, not on the whole image pixels, seems to be relevant. Beside that no stationary condition is required, this approach could also provide the ability to deal with huge-size data as in case of VHR multispectral images. In this paper, our motivation is to exploit the radiometric, spectral as well as spatial information of characteristic pixels to describe textural features from a multispectral image. Then, a weighted graph is constructed to link these feature points based on the similarity between their previous pointwise-based descriptors. Finally, textural features can be characterized and extracted from the spectral domain of this graph. In order to evaluate the performance of the proposed method, a texture-based classification algorithm is implemented. Here, we propose to investigate both the spectral graph clustering and the spectral graph wavelet transform approaches for an unsupervised classification. Experimental results show the effectiveness of our method in terms of classification precision as well as low complexity requirement.