Asymmetric Cross-attention Hierarchical Network Based on CNN and Transformer for Bitemporal Remote Sensing Images Change Detection

As an important task in the field of remote sensing image processing, remote sensing image change detection (CD) has made significant advances through the use of convolutional neural networks (CNN). The Transformer has recently been introduced into the field of CD due to its excellent global percept...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2023-01, Vol.61, p.1-1
Main Authors: Zhang, Xiaofeng, Cheng, Shuli, Wang, Liejun, Li, Haojin
Format: Article
Language:English
Subjects:
Artificial neural networks
asymmetric cross-attention
Asymmetry
Change detection
Change detection (CD)
Computer applications
Consumption
convolutional neural network
deep learning
Detection
Effectiveness
Feature extraction
Image processing
Modules
Neural networks
Remote sensing
Resource consumption
transformer
Transformers
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Summary:As an important task in the field of remote sensing image processing, remote sensing image change detection (CD) has made significant advances through the use of convolutional neural networks (CNN). The Transformer has recently been introduced into the field of CD due to its excellent global perception capabilities. Some works have attempted to combine CNN and Transformer to jointly harvest local-global features. However, these works have not paid much attention to the interaction between the features extracted by both. Also, the use of the Transformer has resulted in significant resource consumption. In this paper, we propose the Asymmetric Cross-attention Hierarchical Network (ACAHNet) by combining CNN and Transformer in a series-parallel manners. The proposed Asymmetric Multi-headed Cross Attention (AMCA) module reduces the quadratic computational complexity of the Transformer to linear, and the module enhances the interaction between features extracted from the CNN and the Transformer. Different from the early and late fusion strategies employed in previous work, the effectiveness of the mid-term fusion strategy employed by ACAHNet shows a new choice of timing for feature fusion in the CD task. Our experiments on the proposed method on three public datasets show that our network has better performance in terms of effectiveness and computational resource consumption compared to other comparative methods.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3245674