Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model

By Yi Liu, Chao Pang, Zongqian Zhan, Xiaomeng Zhang, Xue Yang. IEEE Geoscience and Remote Sensing Letters, 2019

625 citations recorded by the source corpus.

In recent years, building change detection methods have made great progress by introducing deep learning, but they still suffer from the problem of the extracted features not being discriminative enough, resulting in incomplete regions and irregular boundaries. To tackle this problem, we propose a dual-task constrained deep Siamese convolutional network (DTCDSCN) model, which contains three subnetworks: a change detection network and two semantic segmentation networks. DTCDSCN can accomplish both change detection and semantic segmentation at the same time, which can help to learn more discriminative object-level features and obtain a complete change detection map. Furthermore, we introduce a dual attention module (DAM) to exploit the interdependencies between channels and spatial positions, which improves the feature representation. We also improve the focal loss function to suppress the sample imbalance problem. The experimental results obtained with the WHU building data set show that the proposed method is effective for building change detection and achieves state-of-the-art performance in terms of four metrics on the WHU building data set: precision, recall, F1-score, and intersection over union.

Subfields: Atmospheric Science, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/e8b251a42bd5a542793adef1fd9071122c5d03ff

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Citations in the corpus

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…”
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].”
Remote Sensing Image Change Detection With Transformers (2021, methodology): “To increase the RF size, the existing methods include using a deeper CNN model [2], [6]–[8], using dilated convolution [7], and applying attention mechanisms [2], [6], [9]–[13].”
SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection (2022, background): “Liu et al. [24] and Chen et al. [22] introduced dual attention module into the CD task to learn features which contain both channel and spatial information.”
A Transformer-Based Siamese Network for Change Detection (2022, methodology): “We also show better performance in terms of IoU, F1 score, and overall accuracy than recent ConvNet-based (FC-EF, FC-Siam-DI, and FC-Siam-Conc), attention-based (DTCDSCN, STANet, and IFNet), and ConvNet+Transformer-based (BIT) methods.”