Edge-Guided Recurrent Convolutional Neural Network for Multitemporal Remote Sensing Image Building Change Detection

Abstract

Building change detection is a very important application in the field of remote sensing. Recently, deep learning (DL) has been introduced to solve the change detection task and achieved good performance, mainly due to the capability of automatically learning deep features. However, the lack of using prior knowledge (e.g., edge structure information) leads to inaccurate detection results, especially in the areas of building boundaries. To solve this problem, an end-to-end DL method for building change detection, named by edge-guided recurrent convolutional neural network (EGRCNN), is proposed in this article. The main idea is to incorporate both discriminative information and edge structure prior in one framework to improve change detection results, especially to generate more accurate building boundaries. First, a siamese convolutional neural network is trained to simultaneously extract primary multilevel features from multitemporal images. Then, a difference analysis module (DAM) is introduced to further produce discriminative features, which is constructed based on the basic long short-term memory module. Finally, both the discriminative features and the estimated edge structure information are jointly exploited to predict building change map. On one hand, the proposed DAM helps to enhance the discrimination between the changed and unchanged regions. On the other hand, the prior edge information is used to push the predicted changed buildings to preserve the original structure, which can further improve the accuracy of building change detection. Experimental results demonstrate that the performance of the proposed method outperforms several state-of-the-art approaches, in terms of objective metrics and visual comparison results.