An Iterative Feedback-Based Change Detection Algorithm for Flood Mapping in SAR Images

This letter proposes a novel algorithm for the unsupervised detection of flood mapping in synthetic aperture radar (SAR) images. In the literature, unsupervised change detection of SAR images mainly consists of two steps, i.e., first generating a difference image from two given images and then binarizing the difference image to produce the desired change map. Conventional change detection algorithms usually execute these two steps sequentially and separately. In contrast, our algorithm introduces the feedback of the obtained intermediate change maps into both generation and binarization of the difference image. More specifically, we adjust the weights of neighboring pixels in generating the difference image according to the intermediate change maps. With the fed-hack intermediate change maps, we also extend the conventional single binarizing threshold for all pixels of the difference image to threshold maps, i.e., two individual binarizing thresholds are defined for each pixel of the difference image and the threshold maps are adjusted accordingly. Due to such feedback of the intermediate change maps, we may obtain a better difference image and generate more precise change maps, which can surely be fed back again. This iterative execution of the above-mentioned generation and binarization of difference images is terminated when a predefined Markov energy function stops decreasing, i.e., it reaches a local minimum. Experiments with several SAR image data sets with floods show that our algorithm consistently outperforms several state-of-the-art algorithms.