FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer
Remote sensing scene classification (RSSC) is to accurately assign semantic labels to remote sensing images by analyzing scene contents. Recently, many algorithms have made significant progress in improving the classification accuracy of RSSC. However, a large number of parameters and floating point...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11044321/ |
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| author | Wei Wu Xianbin Hu Zhu Li Xueliang Luo |
| author_facet | Wei Wu Xianbin Hu Zhu Li Xueliang Luo |
| author_sort | Wei Wu |
| collection | DOAJ |
| description | Remote sensing scene classification (RSSC) is to accurately assign semantic labels to remote sensing images by analyzing scene contents. Recently, many algorithms have made significant progress in improving the classification accuracy of RSSC. However, a large number of parameters and floating point operations are needed to achieve that end in those approaches, resulting in high complexity. To address the issue, we propose a novel RSSC algorithm, dubbed FishermaskFormer, which aggressively decimates features in the convolutional backbone via a novel masking operation with a proposed fisher discriminant analysis criterion, and then designs a lightweight transformer block to drive the classification loss. This is aimed at offering a flexible and effective framework for preserving classification accuracy while significantly reducing the complexity. The proposed transformer design employs a new grouping index that assigns multiheaded transformer groups by maximizing the information interactions in each group. Compared with leading lightweight RSSC methods, experimental results show this proposed framework achieves higher classification accuracy while having the similar low complexity. |
| format | Article |
| id | doaj-art-5cc5b9b1f8f14e19a31a43231587f03e |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-5cc5b9b1f8f14e19a31a43231587f03e2025-08-20T03:17:24ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118158291584410.1109/JSTARS.2025.358137811044321FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked TransformerWei Wu0https://orcid.org/0000-0002-1301-2575Xianbin Hu1Zhu Li2https://orcid.org/0000-0002-8246-177XXueliang Luo3State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, ChinaState Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, ChinaDepartment of Computer Science & Electrical Engineering, University of Missouri, Kansas City, MO, USAState Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, ChinaRemote sensing scene classification (RSSC) is to accurately assign semantic labels to remote sensing images by analyzing scene contents. Recently, many algorithms have made significant progress in improving the classification accuracy of RSSC. However, a large number of parameters and floating point operations are needed to achieve that end in those approaches, resulting in high complexity. To address the issue, we propose a novel RSSC algorithm, dubbed FishermaskFormer, which aggressively decimates features in the convolutional backbone via a novel masking operation with a proposed fisher discriminant analysis criterion, and then designs a lightweight transformer block to drive the classification loss. This is aimed at offering a flexible and effective framework for preserving classification accuracy while significantly reducing the complexity. The proposed transformer design employs a new grouping index that assigns multiheaded transformer groups by maximizing the information interactions in each group. Compared with leading lightweight RSSC methods, experimental results show this proposed framework achieves higher classification accuracy while having the similar low complexity.https://ieeexplore.ieee.org/document/11044321/Classification accuracyfisher discriminant analysis criterionmodel complexityremote sensing scene classification (RSSC)transformer |
| spellingShingle | Wei Wu Xianbin Hu Zhu Li Xueliang Luo FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Classification accuracy fisher discriminant analysis criterion model complexity remote sensing scene classification (RSSC) transformer |
| title | FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer |
| title_full | FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer |
| title_fullStr | FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer |
| title_full_unstemmed | FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer |
| title_short | FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer |
| title_sort | fishermaskformer lightweight remote sensing scene classification with masked transformer |
| topic | Classification accuracy fisher discriminant analysis criterion model complexity remote sensing scene classification (RSSC) transformer |
| url | https://ieeexplore.ieee.org/document/11044321/ |
| work_keys_str_mv | AT weiwu fishermaskformerlightweightremotesensingsceneclassificationwithmaskedtransformer AT xianbinhu fishermaskformerlightweightremotesensingsceneclassificationwithmaskedtransformer AT zhuli fishermaskformerlightweightremotesensingsceneclassificationwithmaskedtransformer AT xueliangluo fishermaskformerlightweightremotesensingsceneclassificationwithmaskedtransformer |