Change detection with cross enhancement of high‐ and low‐level change‐related features

Abstract

Change detection (CD) is a fundamental yet challenging problem, which aims at detecting changed object in two observations. Recent CD methods are designed based on the off-the-shelf semantic segmentation network architectures, which is not optimal for extracting and using change-related features. In this paper, a novel CD network architecture is proposed, including change-related feature extraction, cross feature enhancement, and multi-level supervision. Absolute difference of the features of different convolutional layers is first computed from a Unet-like network for two observations. The features are partitioned into high- and low-level features according to their functionalities. Then the high- and low-level features are recurrently refined by cross feature enhancement to increase the representational ability of the features. The network learns change-related features with multi-level supervisions. The final CD result can be obtained by fusing multiple predictions. Experimental results on three CD benchmark datasets indicate the superiority of the authors' method when compared with six state-of-the-art deep learning-based CD methods.