Unsupervised change detection based on robust chi-squared transform for bitemporal remotely sensed images

Abstract

Chi-squared transform (CST)-based methods are simple and effective methods for detecting changes in remotely sensed images that have been registered and aligned. The methods operate directly on information stored in the difference image. However, the estimated mean and covariance matrix of the Gaussian distribution that describes the unchanged pixels can be biased when the changed pixels (outliers) are also included. To overcome this issue, we propose a pixel-based unsupervised change detection method that gives robust estimates of these parameters. The method is iterative but requires only a small number of iterations. In addition, we also design an algorithm to automatically search for the optimal threshold that is needed for classifying changed versus unchanged pixels. This algorithm finds the optimal threshold where the mean and covariance matrix of the change detection result most agree with those statistics obtained from the above-mentioned robust algorithm. We refer to our change detection method as the robust CST (RCST) method. The proposed method has been evaluated on two image data-sets and compared with four state-of-the-art methods. The effectiveness of RCST is confirmed by its low overall errors (OE) and high kappa coefficients on both data-sets.