SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images

By Sheng Fang, Kaiyu Li, Jinyuan Shao, Zhe Li. IEEE Geoscience and Remote Sensing Letters, 2021

1,226 citations recorded by the source corpus.

Change detection is an important task in remote sensing (RS) image analysis. It is widely used in natural disaster monitoring and assessment, land resource planning, and other fields. As a pixel-to-pixel prediction task, change detection is sensitive about the utilization of the original position information. Recent change detection methods always focus on the extraction of deep change semantic feature, but ignore the importance of shallow-layer information containing high-resolution and fine-grained features, this often leads to the uncertainty of the pixels at the edge of the changed target and the determination miss of small targets. In this letter, we propose a densely connected siamese network for change detection, namely SNUNet-CD (the combination of Siamese network and NestedUNet). SNUNet-CD alleviates the loss of localization information in the deep layers of neural network through compact information transmission between encoder and decoder, and between decoder and decoder. In addition, Ensemble Channel Attention Module (ECAM) is proposed for deep supervision. Through ECAM, the most representative features of different semantic levels can be refined and used for the final classification. Experimental results show that our method improves greatly on many evaluation criteria and has a better tradeoff between accuracy and calculation amount than other state-of-the-art (SOTA) change detection methods.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/3db31580f60e7277988129cee3df16097f4d5d6b

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Citations in the corpus

ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model (2024, methodology): “…[13] 18.85 86.64 SiamCRNN-34 [13] 28.96 113.28 SiamCRNN-50 [13] 44.45 185.30 SiamCRNN-101 [13] 63.44 224.30 DSIFN [17] 35.73 329.03 SNUNet [32] 10.21 176.36 HANet [33] 2.61 70.68 CGNet [34] 33.68 329.58 T then differentiate the damage levels of buildings despite the varying type of…”
Changer: Feature Interaction is What You Need for Change Detection (2022, methodology): “The classiﬁcation-based method, however, takes change detection as a dense classiﬁcation task directly [6,13,47,48].”
A Transformer-Based Siamese Network for Change Detection (2022, methodology): “• SNUNet [14]: is a multi-level feature concatenation method, in which a densely connected (NestedUNet) Siamese network is used for change detection.”
RS-Mamba for Large Remote Sensing Image Dense Prediction (2024, methodology): “On the CD task, the compared CNN-based models include FC-EF [36], FC-Siam-Diff [36], FC-Siam-Conc [36], STANet [39], DTCDSCN [54], SNUNet [55], CDNet [56], DDCNN [57], DASNet [58], DSIFN [59], and HANet [60], the compared transformer-based models include Change-Former [9], and the CNN-transformer…”
Advancing Plain Vision Transformer Toward Remote Sensing Foundation Model (2022, background): “[11], [12], [13], [14] However, existing studies [15], [16] reveal that the limited receptive field of convolution in each layer makes it difficult for CNNs to pay attention to long-range pixels and extract global context.”
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].”
SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection (2022, methodology): “In [25,26], the authors added dense connections between the features of different layers, so as to enhance the capability of the CD network.”