Automatic change detection in high-resolution remote-sensing images by means of level set evolution and support vector machine classification

In this article, we propose a method for change detection in high-resolution remote-sensing images by means of level set evolution and support vector machine (SVM) classification, which combined both the pixel-level method and the object-level method. Both pixel-based change features and object-based ones are extracted to improve the discriminability between the changed class and the unchanged class. At the pixel level, the change detection problem is formulated as a segmentation issue using level set evolution in the difference images. At the object level, potential training samples are selected from the segmentation results without manual intervention into the SVM classifier. Thereafter, the final changes are obtained by combining the pixel-based changes and the object-based changes. A chief advantage of our approach is being able to select appropriate samples for SVM classifier training. Furthermore, our proposed method helps improve the accuracy and the degree of automation. We systematically evaluate it with various Satellite Pour l'Observation de la Terre (SPOT) 5 images and aerial images. Experimental results demonstrate the accuracy of our proposed method.