DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images

Abstract

Change detection is a basic task of remote sensing image processing. The research objective is to identity the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise of deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information. To overcome the lack of resistance of current methods to pseudo-changes, in this paper, we propose a new method, namely, dual attentive fully convolutional Siamese networks (DASNet) for change detection in high-resolution images. Through the dual-attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e. unchanged samples are much more than changed samples, which is one of the main reasons resulting in pseudo-changes. We put forward the weighted double margin contrastive loss to address this problem by punishing the attention to unchanged feature pairs and increase attention to changed feature pairs. The experimental results of our method on the change detection dataset (CDD) and the building change detection dataset (BCDD) demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.1\% and 3.6\%, respectively, in the F1 score. Our Pytorch implementation is available at https://github.com/lehaifeng/DASNet.

12 pages, 13 figures, 5 tables

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Self-Attention Generative Adversarial Networks
• A Structured Self-attentive Sentence Embedding
• Deeply-Supervised Nets
• Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection

Cited by in corpus (19)

• Remote Sensing Image Change Detection with Transformers
• S2Looking: A Satellite Side-Looking Dataset for Building Change Detection
• An Empirical Study of Remote Sensing Pretraining
• Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions
• Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
• Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images
• Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images
• BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
• Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection
• A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges
• RDP-Net: Region Detail Preserving Network for Change Detection
• Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection
• SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images
• Continuous Cross-resolution Remote Sensing Image Change Detection
• MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images
• WRICNet:A Weighted Rich-scale Inception Coder Network for Multi-Resolution Remote Sensing Image Change Detection
• Towards Deep and Efficient: A Deep Siamese Self-Attention Fully Efficient Convolutional Network for Change Detection in VHR Images
• HA2F: Dual-module Collaboration-Guided Hierarchical Adaptive Aggregation Framework for Remote Sensing Change Detection
• Looking for change? Roll the Dice and demand Attention