Local spectral-spatial clustering for remote sensing imagery

Remote sensing image clustering is a challenging task. Recently, by combining the spectral and spatial information of remote sensing data, the clustering accuracy can be dramatically enhanced. However, it has always been difficult to determine the weight parameter for balancing the spectral and spatial terms of the clustering objective function. In this paper, spectral-spatial clustering with a local weight parameter determination method for remote sensing imagery is proposed, i.e. LSSC. In LSSC, considering the large scale of remote sensing images, the weight parameter is determined locally in a patch image instead of the whole image. The local weight parameter is then used in constructing the objective function of LSSC. Thus, the remote sensing image clustering problem is transformed into an optimization problem. Finally, in order to achieve a better optimization performance, a variant of differential evolution (i.e. jDE) is used as the optimizer due to its powerful optimization capability. Experimental results confirm that the proposed LSSC can acquire a higher clustering accuracy than other spectral-spatial clustering methods.