Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification
As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challeng...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/773 |
| _version_ | 1797437325180928000 |
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| author | Chuan Tang Xiao Zheng Chang Tang |
| author_facet | Chuan Tang Xiao Zheng Chang Tang |
| author_sort | Chuan Tang |
| collection | DOAJ |
| description | As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challenges that hinder the practical applications of RSSC, such as complex composition of land cover, scale-variation of objects, and redundant and noisy areas for scene classification. In order to mitigate the impact of these issues, we propose an adaptive discriminative regions learning network for RSSC, referred as ADRL-Net briefly, which locates discriminative regions effectively for boosting the performance of RSSC by utilizing a novel self-supervision mechanism. Our proposed ADRL-Net consists of three main modules, including a discriminative region generator, a region discriminator, and a region scorer. Specifically, the discriminative region generator first generates some candidate regions which could be informative for RSSC. Then, the region discriminator evaluates the regions generated by region generator and provides feedback for the generator to update the informative regions. Finally, the region scorer makes prediction scores for the whole image by using the discriminative regions. In such a manner, the three modules of ADRL-Net can cooperate with each other and focus on the most informative regions of an image and reduce the interference of redundant regions for final classification, which is robust to the complex scene composition, object scales, and irrelevant information. In order to validate the efficacy of the proposed network, we conduct experiments on four widely used benchmark datasets, and the experimental results demonstrate that ADRL-Net consistently outperforms other state-of-the-art RSSC methods. |
| first_indexed | 2024-03-09T11:17:36Z |
| format | Article |
| id | doaj.art-bd31c5e0131549f9ad0d193d7d81a871 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:17:36Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bd31c5e0131549f9ad0d193d7d81a8712023-12-01T00:27:22ZengMDPI AGSensors1424-82202023-01-0123277310.3390/s23020773Adaptive Discriminative Regions Learning Network for Remote Sensing Scene ClassificationChuan Tang0Xiao Zheng1Chang Tang2School of Computer Science, China University of Geosciences, No. 68 Jincheng Road, Wuhan 430078, ChinaSchool of Computer, National University of Defense Technology, Deya Road, Changsha 410073, ChinaSchool of Computer Science, China University of Geosciences, No. 68 Jincheng Road, Wuhan 430078, ChinaAs an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challenges that hinder the practical applications of RSSC, such as complex composition of land cover, scale-variation of objects, and redundant and noisy areas for scene classification. In order to mitigate the impact of these issues, we propose an adaptive discriminative regions learning network for RSSC, referred as ADRL-Net briefly, which locates discriminative regions effectively for boosting the performance of RSSC by utilizing a novel self-supervision mechanism. Our proposed ADRL-Net consists of three main modules, including a discriminative region generator, a region discriminator, and a region scorer. Specifically, the discriminative region generator first generates some candidate regions which could be informative for RSSC. Then, the region discriminator evaluates the regions generated by region generator and provides feedback for the generator to update the informative regions. Finally, the region scorer makes prediction scores for the whole image by using the discriminative regions. In such a manner, the three modules of ADRL-Net can cooperate with each other and focus on the most informative regions of an image and reduce the interference of redundant regions for final classification, which is robust to the complex scene composition, object scales, and irrelevant information. In order to validate the efficacy of the proposed network, we conduct experiments on four widely used benchmark datasets, and the experimental results demonstrate that ADRL-Net consistently outperforms other state-of-the-art RSSC methods.https://www.mdpi.com/1424-8220/23/2/773remote sensingscene classificationdeep convolutional neural networksRSSCDCNNs |
| spellingShingle | Chuan Tang Xiao Zheng Chang Tang Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification Sensors remote sensing scene classification deep convolutional neural networks RSSC DCNNs |
| title | Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification |
| title_full | Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification |
| title_fullStr | Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification |
| title_full_unstemmed | Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification |
| title_short | Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification |
| title_sort | adaptive discriminative regions learning network for remote sensing scene classification |
| topic | remote sensing scene classification deep convolutional neural networks RSSC DCNNs |
| url | https://www.mdpi.com/1424-8220/23/2/773 |
| work_keys_str_mv | AT chuantang adaptivediscriminativeregionslearningnetworkforremotesensingsceneclassification AT xiaozheng adaptivediscriminativeregionslearningnetworkforremotesensingsceneclassification AT changtang adaptivediscriminativeregionslearningnetworkforremotesensingsceneclassification |