Formula omitted]-cut induced Fuzzy Deep Neural Network for change detection of SAR images

Abstract

Change detection (CD) is a process of identifying dissimilarities from two or more co-registered multitemporal images. In this paper, we have introduced a α-cut induced Fuzzy layer to the Deep Neural Network (αFDNN). Deep neural networks for change detection normally rely on the pre-classified labels of the clustering. But the pre-classified labels are more coarse and ambiguous, which is not able to highlight the changed information accurately. This challenge can be addressed by encapsulating the local information and fuzzy logic into the deep neural network. This takes the advantage of enhancing the changed information and of reducing the effect of speckle noise. As the first step in change detection, a fused difference image is generated from the mean and log ratio image with the advent of Stationary Wavelet Transform (SWT). It not only eliminates the impact of speckle noise but also it has good ability to identify the trend of change thanks to the shift invariance property. Pseudo classification is performed as the next step using Fuzzy C Means (FCM) clustering. Then, we apply reformulated α-cut induced Fuzzy Deep Neural Network to generate the final change map which facilitates a final representation of data more suitable for the process of classification and clustering. It also results into a noteworthy improvement in the change detection result. The efficacy of the algorithm is analyzed through the parameter study. Experimental results on three Synthetic Aperture Radar (SAR) datasets demonstrate the superior performance of the proposed method compared to state-of-the art change detection methods.