A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection
Change detection methods play an indispensable role in remote sensing. Some change detection methods have obtained a fairly good performance by introducing attention mechanism on the basis of the convolutional neural network (CNN), but identifying intricate changes remains difficult. In response to...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9855775/ |
| _version_ | 1811281291090329600 |
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| author | Wei Wang Xinai Tan Peng Zhang Xin Wang |
| author_facet | Wei Wang Xinai Tan Peng Zhang Xin Wang |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | Change detection methods play an indispensable role in remote sensing. Some change detection methods have obtained a fairly good performance by introducing attention mechanism on the basis of the convolutional neural network (CNN), but identifying intricate changes remains difficult. In response to these problems, this article proposes a new model for detecting changes in remote sensing, namely, MTCNet, which combines the advantages of multiscale transformer with the convolutional block attention module (CBAM) to improve the detection quality of different remote sensing images. On the basis of traditional convolutions, the transformer module is introduced to extract bitemporal image features by modeling contextual information. Based on the transformer module, a multiscale module is designed to form a multiscale transformer, which can obtain features at different scales in bitemporal images, thereby identifying the changes we are interested in. Based on the multiscale transformer module, the CBAM is introduced. The CBAM is split into a spatial attention module and a channel attention module, which are applied to the front and back ends of the multiscale transformer, respectively. Spatial information and channel information of feature maps are modeled separately. In this article, the validity and efficiency of the method are verified by a large number of experiments on the LEVIR-CD dataset and the WHU-CD dataset. |
| first_indexed | 2024-04-13T01:29:57Z |
| format | Article |
| id | doaj.art-7230eac878134f0f86ca6a74a1c38065 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-13T01:29:57Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-7230eac878134f0f86ca6a74a1c380652022-12-22T03:08:32ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-01156817682510.1109/JSTARS.2022.31985179855775A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change DetectionWei Wang0https://orcid.org/0000-0002-2298-3429Xinai Tan1Peng Zhang2Xin Wang3https://orcid.org/0000-0003-2386-5405School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, ChinaSchool of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, ChinaChange detection methods play an indispensable role in remote sensing. Some change detection methods have obtained a fairly good performance by introducing attention mechanism on the basis of the convolutional neural network (CNN), but identifying intricate changes remains difficult. In response to these problems, this article proposes a new model for detecting changes in remote sensing, namely, MTCNet, which combines the advantages of multiscale transformer with the convolutional block attention module (CBAM) to improve the detection quality of different remote sensing images. On the basis of traditional convolutions, the transformer module is introduced to extract bitemporal image features by modeling contextual information. Based on the transformer module, a multiscale module is designed to form a multiscale transformer, which can obtain features at different scales in bitemporal images, thereby identifying the changes we are interested in. Based on the multiscale transformer module, the CBAM is introduced. The CBAM is split into a spatial attention module and a channel attention module, which are applied to the front and back ends of the multiscale transformer, respectively. Spatial information and channel information of feature maps are modeled separately. In this article, the validity and efficiency of the method are verified by a large number of experiments on the LEVIR-CD dataset and the WHU-CD dataset.https://ieeexplore.ieee.org/document/9855775/Change detectionconvolutional block attention module (CBAM)multiscaleremote sensingtransformer |
| spellingShingle | Wei Wang Xinai Tan Peng Zhang Xin Wang A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change detection convolutional block attention module (CBAM) multiscale remote sensing transformer |
| title | A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection |
| title_full | A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection |
| title_fullStr | A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection |
| title_full_unstemmed | A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection |
| title_short | A CBAM Based Multiscale Transformer Fusion Approach for Remote Sensing Image Change Detection |
| title_sort | cbam based multiscale transformer fusion approach for remote sensing image change detection |
| topic | Change detection convolutional block attention module (CBAM) multiscale remote sensing transformer |
| url | https://ieeexplore.ieee.org/document/9855775/ |
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