Laplacian Pyramid Network With Hybrid Encoder and Edge Guidance for Remote Sensing Change Detection

Abstract

Remote sensing change detection (CD) is a crucial task for observing and analyzing dynamic land cover alterations. Many CD methods based on deep learning demonstrate strong performance, but their effectiveness is influenced by the choice of encoder and the challenge of accurately delineating the edges of change regions. In this article, we propose a Laplacian pyramid network with hybrid encoder and edge guidance (HE-LPNet) to solve these issues. Specifically, the hybrid encoder combines the advantages of convolutional neural networks and transformer, resulting in extracted features that are more fine-grained. Meanwhile, the hybrid encoder incorporates the vision foundation models, leading to enhanced generalization of the overall model. In addition to feature extraction, the image is processed to generate a Laplacian pyramid, which is then fused with the features extracted by the hybrid encoder to enhance the salient features at the pixel-level. In the decoder stage, weighted guided attention is designed to selectively apply channel and spatial attention to the fused features, improving the network's ability to discriminate change regions. Furthermore, we present an edge-guided loss to capture edge information in change regions. To validate the effectiveness of the proposed HE-LPNet, extensive experiments are conducted on three high-resolution remote sensing CD datasets. The experimental results demonstrate that our method surpasses other state-of-the-art CD methods.