RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing Imagery
Change detection (CD) methods work on the basis of co-registered multi-temporal images with equivalent resolutions. Due to the limitation of sensor imaging conditions and revisit period, it is difficult to acquire the desired images, especially in emergency situations. In addition, accurate multi-te...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/18/4527 |
| _version_ | 1797482850378842112 |
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| author | Juan Tian Daifeng Peng Haiyan Guan Haiyong Ding |
| author_facet | Juan Tian Daifeng Peng Haiyan Guan Haiyong Ding |
| author_sort | Juan Tian |
| collection | DOAJ |
| description | Change detection (CD) methods work on the basis of co-registered multi-temporal images with equivalent resolutions. Due to the limitation of sensor imaging conditions and revisit period, it is difficult to acquire the desired images, especially in emergency situations. In addition, accurate multi-temporal images co-registration is largely limited by vast object changes and matching algorithms. To this end, a resolution- and alignment-aware change detection network (RACDNet) is proposed for multi-resolution optical remote-sensing imagery CD. In the first stage, to generate high-quality bi-temporal images, a light-weighted super-resolution network is proposed by fully considering the construction difficulty of different regions, which facilitates to detailed information recovery. Adversarial loss and perceptual loss are further adopted to improve the visual quality. In the second stage, deformable convolution units are embedded in a novel Siamese–UNet architecture for bi-temporal deep features alignment; thus, robust difference features can be generated for change information extraction. We further use an atrous convolution module to enlarge the receptive field, and an attention module to bridge the semantic gap between the encoder and decoder. To verify the effectiveness of our RACDNet, a novel multi-resolution change detection dataset (MRCDD) is created by using Google Earth. The quantitative and qualitative experimental results demonstrate that our RACDNet is capable of enhancing the details of the reconstructed images significantly, and the performance of CD surpasses other state-of-the-art methods by a large margin. |
| first_indexed | 2024-03-09T22:38:24Z |
| format | Article |
| id | doaj.art-1c521736f26f40af80b1b4227aaf77fd |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T22:38:24Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1c521736f26f40af80b1b4227aaf77fd2023-11-23T18:44:06ZengMDPI AGRemote Sensing2072-42922022-09-011418452710.3390/rs14184527RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing ImageryJuan Tian0Daifeng Peng1Haiyan Guan2Haiyong Ding3School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaChange detection (CD) methods work on the basis of co-registered multi-temporal images with equivalent resolutions. Due to the limitation of sensor imaging conditions and revisit period, it is difficult to acquire the desired images, especially in emergency situations. In addition, accurate multi-temporal images co-registration is largely limited by vast object changes and matching algorithms. To this end, a resolution- and alignment-aware change detection network (RACDNet) is proposed for multi-resolution optical remote-sensing imagery CD. In the first stage, to generate high-quality bi-temporal images, a light-weighted super-resolution network is proposed by fully considering the construction difficulty of different regions, which facilitates to detailed information recovery. Adversarial loss and perceptual loss are further adopted to improve the visual quality. In the second stage, deformable convolution units are embedded in a novel Siamese–UNet architecture for bi-temporal deep features alignment; thus, robust difference features can be generated for change information extraction. We further use an atrous convolution module to enlarge the receptive field, and an attention module to bridge the semantic gap between the encoder and decoder. To verify the effectiveness of our RACDNet, a novel multi-resolution change detection dataset (MRCDD) is created by using Google Earth. The quantitative and qualitative experimental results demonstrate that our RACDNet is capable of enhancing the details of the reconstructed images significantly, and the performance of CD surpasses other state-of-the-art methods by a large margin.https://www.mdpi.com/2072-4292/14/18/4527change detectionsuper-resolutionsiamese–UNetdeformable convolutionfeature alignmentatrous convolution |
| spellingShingle | Juan Tian Daifeng Peng Haiyan Guan Haiyong Ding RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing Imagery Remote Sensing change detection super-resolution siamese–UNet deformable convolution feature alignment atrous convolution |
| title | RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing Imagery |
| title_full | RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing Imagery |
| title_fullStr | RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing Imagery |
| title_full_unstemmed | RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing Imagery |
| title_short | RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing Imagery |
| title_sort | racdnet resolution and alignment aware change detection network for optical remote sensing imagery |
| topic | change detection super-resolution siamese–UNet deformable convolution feature alignment atrous convolution |
| url | https://www.mdpi.com/2072-4292/14/18/4527 |
| work_keys_str_mv | AT juantian racdnetresolutionandalignmentawarechangedetectionnetworkforopticalremotesensingimagery AT daifengpeng racdnetresolutionandalignmentawarechangedetectionnetworkforopticalremotesensingimagery AT haiyanguan racdnetresolutionandalignmentawarechangedetectionnetworkforopticalremotesensingimagery AT haiyongding racdnetresolutionandalignmentawarechangedetectionnetworkforopticalremotesensingimagery |