A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection

By Qian Shi, Mengxi Liu, Shengchen Li, Xiaoping Liu, Fei Wang, Liangpei Zhang. IEEE Transactions on Geoscience and Remote Sensing, 2021

631 citations recorded by the source corpus.

Change detection (CD) aims to identify surface changes from bitemporal images. In recent years, deep learning (DL)-based methods have made substantial breakthroughs in the field of CD. However, CD results can be easily affected by external factors, including illumination, noise, and scale, which leads to pseudo-changes and noise in the detection map. To deal with these problems and achieve more accurate results, a deeply supervised (DS) attention metric-based network (DSAMNet) is proposed in this article. A metric module is employed in DSAMNet to learn change maps by means of deep metric learning, in which convolutional block attention modules (CBAM) are integrated to provide more discriminative features. As an auxiliary, a DS module is introduced to enhance the feature extractor’s learning ability and generate more useful features. Moreover, another challenge encountered by data-driven DL algorithms is posed by the limitations in change detection datasets (CDDs). Therefore, we create a CD dataset, Sun Yat-Sen University (SYSU)-CD, for bitemporal image CD, which contains a total of 20 000 aerial image pairs of size $256\times256$ . Experiments are conducted on both the CDD and the SYSU-CD dataset. Compared to other state-of-the-art methods, our network achieves the highest accuracy on both datasets, with an F1 of 93.69% on the CDD dataset and 78.18% on the SYSU-CD dataset.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/52b20919e53bcc852aa7ed52c4e7b922dfe2a27b

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Citations in the corpus

Change Detection on Remote Sensing Images Using Dual-Branch Multilevel Intertemporal Network (2023, background): “Moreover, several schemes are extensively investigated and adopted to expand the scope of the receptive field, including deepened network architectures [37], dilated convolutional strategies [39], and attention mechanisms [35], [36], [37], [40].”
A Transformer-Based Siamese Network for Change Detection (2022, background): “As a result, CD models with stacked convolution layers, dilated convolutions, and attention mechanisms (channel and spatial attention) have been proposed [1].”
Transformers in Remote Sensing: A Survey (2022, methodology): “The work of [108] introduces a topic token-based mask transformers 11 12 Method Task Datasets Metrics Highlights MSPSNet [55] Change Detection SYSU-CD [97], LEVIR [61] F1 score Introduces a multi-scale Siamese framework employing a parallel convolutional structure for feature integration of differen”
ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model (2024, methodology): “After the early days of developing image patch-based approaches with tiny deep learning models [12]– [14], the released of a number of large-scale benchmark datasets [15] allowed us to start designing and introducing even larger and deeper models.”
Changer: Feature Interaction is What You Need for Change Detection (2022, methodology): “In general, the metric-based method transforms the two input images into a feature space whose feature representation becomes more consistent [27, 32].”