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In this paper, a novel superpixel-based approach is introduced for unsupervised change detection using remote sensing images. Theproposed approach contains three steps: 1) Superpixel segmentation. The simple linear iterative cluster (SLIC) algorithm is applied toobtain lattice-like homogenous superpixels. To avoid discordances of the superpixel boundaries obtained from bi-temporal images, thetwo images are firstly fused using principle component analysis. And then, the SLIC algorithm is applied on the first three principlecomponents, which contain the main information of the two images. 2) For each superpixel, which is considered as the basic unit of theimage space, the multi-dimensional change vector is computed from spectral, textural and structural features. 3) The superpixels areclassified into two type: changed and unchanged through two progressive classification processes. The superpixels are firstly catalogedinto three types: changed, unchanged and undefined by thresholding the change vectors and a voting process. And then the undefinedsuperpixels are further classified into two classes: changed and unchanged, using a SVM-based classifier, which is trained by thederived changed and unchanged superpixels from the former step. The experiment using Indonesia data set has confirmed that theproposed approach is able to detect the changes automatically, by exploiting multiple change features

SUPERPIXEL-BASED UNSUPERVISED CHANGE DETECTION USING MULTI-DIMENSIONAL CHANGE VECTOR ANALYSIS AND SVM-BASED CLASSIFICATION