Spectral-Spatial Clustering with a Local Weight Parameter Determination Method for Remote Sensing Imagery

Remote sensing image clustering is a challenging task considering its intrinsic complexity. Recently, by combining the spectral and spatial information of the remote sensing data, the clustering performance can be dramatically enhanced, termed as Spectral-Spatial Clustering (SSC). However, it has always been difficult to determine the weight parameter for balancing the spectral term and spatial term of the clustering objective function. In this paper, spectral-spatial clustering with a local weight parameter determination method for remote sensing image was proposed, i.e., L-SSC. In L-SSC, considering the large scale of remote sensing images, the weight parameter can be determined locally in a patch image instead of the whole image. Afterwards, the local weight parameter was used in constructing the objective function of L-SSC. Thus, the remote sensing image clustering problem was transformed into an optimization problem. Finally, in order to achieve a better optimization performance, a variant of differential evolution (i.e., jDE) was used as the optimizer due to its powerful optimization capability. Experimental results on three remote sensing images, including a Wuhan TM image, a Fancun Quickbird image, and an Indian Pine AVIRIS image, demonstrated that the proposed L-SSC can acquire higher clustering accuracy in comparison to other spectral-spatial clustering methods.