An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory
Change detection (CD) is a meaningful and challenging task for remote sensing (RS) image analysis. Deep learning (DL) based methods have shown great potential in change detection tasks, there are still two problems with existing deep learning methods such as CNN and Transformer: (1) They do not targ...
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Elsevier
2024-12-01
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| Series: | Applied Computing and Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197424000570 |
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| author | Yanbo Sun Wenxing Bao Wei Feng Kewen Qu Xuan Ma Xiaowu Zhang |
| author_facet | Yanbo Sun Wenxing Bao Wei Feng Kewen Qu Xuan Ma Xiaowu Zhang |
| author_sort | Yanbo Sun |
| collection | DOAJ |
| description | Change detection (CD) is a meaningful and challenging task for remote sensing (RS) image analysis. Deep learning (DL) based methods have shown great potential in change detection tasks, there are still two problems with existing deep learning methods such as CNN and Transformer: (1) They do not target different depths to extract global semantics in the network; (2) The increase in network depth will lead to uncertainty in the edge pixels of changing targets and the absence of small targets. First, to address this challenge and address these issues, this work proposes a global pyramid aggregation UNet3+ (GPA-UNet3+) change detection model, that uses UNet3+ as the backbone network and connects the encoder and decoder with a pyramid structure. Secondly, a Global Atrous Spatial Pooling Pyramid Module (GASPPM) is proposed. Refined features at different depths and aggregated them to enhance the network’s ability to extract global semantics. Finally, the Edge Enhancement Channel Attention Module (EECAM) is specifically proposed to alleviate the edge pixel uncertainty and spatial position information loss caused by the increase in network depth. Multiple experiments are conducted on two common change detection datasets and a real dataset. Extensive experimental results show that the proposed method outperforms other state-of-the-art methods, achieving the highest F1-score of 90.95%, 95.31%, and 88.32% on the LEVIR-CD dataset, SVCD dataset and Shizuishan Mining Area dataset, respectively. |
| format | Article |
| id | doaj-art-efeb1ba2e10945dfbed8c1c96f0584ab |
| institution | Kabale University |
| issn | 2590-1974 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj-art-efeb1ba2e10945dfbed8c1c96f0584ab2024-12-18T08:51:50ZengElsevierApplied Computing and Geosciences2590-19742024-12-0124100210An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratoryYanbo Sun0Wenxing Bao1Wei Feng2Kewen Qu3Xuan Ma4Xiaowu Zhang5School of Computer Science and Engineering, North Minzu University, YinChuan, 750021, ChinaSchool of Computer Science and Engineering, North Minzu University, YinChuan, 750021, China; Corresponding author.School of Electronic Engineering, Xidian University, Xi’an, 710071, ChinaSchool of Computer Science and Engineering, North Minzu University, YinChuan, 750021, ChinaSchool of Computer Science and Engineering, North Minzu University, YinChuan, 750021, ChinaSchool of Computer Science and Engineering, North Minzu University, YinChuan, 750021, ChinaChange detection (CD) is a meaningful and challenging task for remote sensing (RS) image analysis. Deep learning (DL) based methods have shown great potential in change detection tasks, there are still two problems with existing deep learning methods such as CNN and Transformer: (1) They do not target different depths to extract global semantics in the network; (2) The increase in network depth will lead to uncertainty in the edge pixels of changing targets and the absence of small targets. First, to address this challenge and address these issues, this work proposes a global pyramid aggregation UNet3+ (GPA-UNet3+) change detection model, that uses UNet3+ as the backbone network and connects the encoder and decoder with a pyramid structure. Secondly, a Global Atrous Spatial Pooling Pyramid Module (GASPPM) is proposed. Refined features at different depths and aggregated them to enhance the network’s ability to extract global semantics. Finally, the Edge Enhancement Channel Attention Module (EECAM) is specifically proposed to alleviate the edge pixel uncertainty and spatial position information loss caused by the increase in network depth. Multiple experiments are conducted on two common change detection datasets and a real dataset. Extensive experimental results show that the proposed method outperforms other state-of-the-art methods, achieving the highest F1-score of 90.95%, 95.31%, and 88.32% on the LEVIR-CD dataset, SVCD dataset and Shizuishan Mining Area dataset, respectively.http://www.sciencedirect.com/science/article/pii/S2590197424000570Change detectionConvolutional neural networksUNet3+Global pyramid aggregationEdge enhancementRemote sensing |
| spellingShingle | Yanbo Sun Wenxing Bao Wei Feng Kewen Qu Xuan Ma Xiaowu Zhang An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory Applied Computing and Geosciences Change detection Convolutional neural networks UNet3+ Global pyramid aggregation Edge enhancement Remote sensing |
| title | An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory |
| title_full | An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory |
| title_fullStr | An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory |
| title_full_unstemmed | An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory |
| title_short | An UNet3+ Network based on global pyramid aggregation for change detection in optical remote-sensing imagesGosNIIASLEarning, VIsion and Remote sensing laboratory |
| title_sort | unet3 network based on global pyramid aggregation for change detection in optical remote sensing imagesgosniiaslearning vision and remote sensing laboratory |
| topic | Change detection Convolutional neural networks UNet3+ Global pyramid aggregation Edge enhancement Remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S2590197424000570 |
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