Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery

Abstract

The rapid advancement of automated artificial intelligence algorithms and remote sensing instruments has benefited change detection (CD) tasks. However, there is still a lot of space to study for precise detection, especially the edge integrity and internal holes phenomenon of change features. In order to solve these problems, we design the Change Guiding Network (CGNet), to tackle the insufficient expression problem of change features in the conventional U-Net structure adopted in previous methods, which causes inaccurate edge detection and internal holes. Change maps from deep features with rich semantic information are generated and used as prior information to guide multi-scale feature fusion, which can improve the expression ability of change features. Meanwhile, we propose a self-attention module named Change Guide Module (CGM), which can effectively capture the long-distance dependency among pixels and effectively overcome the problem of the insufficient receptive field of traditional convolutional neural networks. On four major CD datasets, we verify the usefulness and efficiency of the CGNet, and a large number of experiments and ablation studies demonstrate the effectiveness of CGNet. We're going to open-source our code at https://github.com/ChengxiHAN/CGNet-CD.

References in corpus (9)

• Decoupled Weight Decay Regularization
• Remote Sensing Image Change Detection with Transformers
• Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
• HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images
• S2Looking: A Satellite Side-Looking Dataset for Building Change Detection
• An Empirical Study of Remote Sensing Pretraining
• ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias
• Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning
• Towards Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentation

Cited by in corpus (5)

• Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
• C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images
• ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer
• EfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers Exchanged
• Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection