Change Detection in Landsat Images Using Unsupervised Learning and RBF-Based Clustering

Change detection is one of the extensively investigated problems in the field of remote sensing. Generally, it involves the analysis of multitemporal remote sensing images of the same sensor or different sensors. The nonnormalizable radiometric differences, which are added due to environmental interferences, could lead to increase the intra-class variability in multitemporal images, and hence, the change detection problem would be nonlinear. To cope with these issues, a novel unsupervised manifold learning based change detection technique is proposed, which discovers the nonlinear structure of data hidden in original space. The proposed framework consists of two processing stages. In the first stage, a novel orthogonal unsupervised discriminant projection (OUDP) based technique is proposed for feature extraction. The orthogonal basis vectors, obtained from OUDP, are able to provide discriminant features. In addition, in the second stage, a novel radial-basis function based clustering is presented, which yields the better clustering than the single clustering approach. Moreover, an extended kernel-weighted OUDP technique is also presented, which improves the performance of OUDP technique. Particularity of the proposed framework resides on following three things. First one is the creation of eigenvector space by using the nonoverlapping blocks of the image. Second one is the extraction of features by exploiting the local neighborhood information around each pixel, this generates clear or spot free change maps. The last one is the RBF stage of clustering technique, which greatly improves the classification of the unchanged and changed pixels. Experimental results on multispectral images of different sensors validate the effectiveness of the proposed method.