DCAT: Dual Cross-Attention-Based Transformer for Change Detection

Several transformer-based methods for change detection (CD) in remote sensing images have been proposed, with Siamese-based methods showing promising results due to their two-stream feature extraction structure. However, these methods ignore the potential of the cross-attention mechanism to improve...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-05, Vol.15 (9), p.2395
Main Authors: Zhou, Yuan, Huo, Chunlei, Zhu, Jiahang, Huo, Leigang, Pan, Chunhong
Format: Article
Language:English
Subjects:
Change detection
Coders
Cognition & reasoning
Computational linguistics
Deep learning
dual cross-attention
Electric transformers
Feature extraction
Image enhancement
Language processing
Natural language interfaces
Remote sensing
Semantics
Temporal variations
transformer
Transformers
Visual discrimination
Visual observation
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Summary:Several transformer-based methods for change detection (CD) in remote sensing images have been proposed, with Siamese-based methods showing promising results due to their two-stream feature extraction structure. However, these methods ignore the potential of the cross-attention mechanism to improve change feature discrimination and thus, may limit the final performance. Additionally, using either high-frequency-like fast change or low-frequency-like slow change alone may not effectively represent complex bi-temporal features. Given these limitations, we have developed a new approach that utilizes the dual cross-attention-transformer (DCAT) method. This method mimics the visual change observation procedure of human beings and interacts with and merges bi-temporal features. Unlike traditional Siamese-based CD frameworks, the proposed method extracts multi-scale features and models patch-wise change relationships by connecting a series of hierarchically structured dual cross-attention blocks (DCAB). DCAB is based on a hybrid dual branch mixer that combines convolution and transformer to extract and fuse local and global features. It calculates two types of cross-attention features to effectively learn comprehensive cues with both low- and high-frequency information input from paired CD images. This helps enhance discrimination between the changed and unchanged regions during feature extraction. The feature pyramid fusion network is more lightweight than the encoder and produces powerful multi-scale change representations by aggregating features from different layers. Experiments on four CD datasets demonstrate the advantages of DCAT architecture over other state-of-the-art methods.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15092395