Contour-enhanced densely connected Siamese network for change detection

Abstract

Obtaining change information in different periods from a pair of registered satellite remote sensing images is of great significance to urban planning, so change detection (CD) technology has attracted extensive attention in recent years. In recent years, convolutional neural networks have set off a boom in many artificial intelligence research fields because of their excellent feature extraction performance. However, the common convolution operation mainly focuses on the abstraction of the semantic information of the features, which often leads to the details of the features being ignored and thus affects the final accuracy. For example, the contour details of changing objects and the structural information of small objects are often lost. We propose a Siamese network that enhances contour and structural details to achieve higher-accuracy CD tasks for bitemporal remote sensing images. In this network, we propose an efficient contour-enhanced convolutional block that is based on the reparameterization technique. The contour-enhanced convolutional block strengthens the extraction of structural and contour features by integrating different branches. In addition, inspired by NestedUNet and to better preserve the original location information of features, we use a dense connection as the feature extractor to obtain refined features of bitemporal images. After that, we use a difference module to calculate the change characteristics of the dual-time image, and we use atrous spatial pyramid pooling and enhanced spatial attention to further refine the obtained change characteristics. We conduct extensive experiments on three different datasets to verify the effectiveness of our model. Experimental results show that our method outperforms state-of-the-art methods in both overall accuracy and visualization details.