Unsupervised Change Detection in an Urban Environment Using Multitemporal PolSAR Images

In this paper we address the problem of change detection in multitemporal polarimetric synthetic aperture radar (PolSAR) images of an urban environment. To compare PolSAR images acquired on two different dates, a change image was produced by using a modified ratio operator and normalized difference ratio operator. The amplitudes of three polarimetric components (HH, HV and VV) and the diagonal elements of the coherency matrix (T-11, T-22 and T-33) from both dates were used to generate the change image. A thresholding algorithm based on histogram fitting was then implemented to automatically classify the change image into two classes: change and no-change. Experiments were carried out on two sets of multitemporal images acquired by ALOS PALSAR to confirm the effectiveness of the proposed unsupervised approach. The combination of the normalized difference ratio operator with the diagonal elements of the coherency matrix is better suited for change detection than any other combination.