Fusion of Infrared and Visible Images for Remote Detection of Low-Altitude Slow-Speed Small Targets
Detection of the low-altitude and slow-speed small (LSS) targets is one of the most popular research topics in remote sensing. Despite of a few existing approaches, there is still an accuracy gap for satisfying the practical needs. As the LSS targets are too small to extract useful features, deep le...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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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/9362227/ |
| _version_ | 1819011705807568896 |
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| author | Haijiang Sun Qiaoyuan Liu Jiacheng Wang Jinchang Ren Yanfeng Wu Huimin Zhao Huakang Li |
| author_facet | Haijiang Sun Qiaoyuan Liu Jiacheng Wang Jinchang Ren Yanfeng Wu Huimin Zhao Huakang Li |
| author_sort | Haijiang Sun |
| collection | DOAJ |
| description | Detection of the low-altitude and slow-speed small (LSS) targets is one of the most popular research topics in remote sensing. Despite of a few existing approaches, there is still an accuracy gap for satisfying the practical needs. As the LSS targets are too small to extract useful features, deep learning based algorithms can hardly be used. To this end, we propose in this article an effective strategy for determining the region of interest, using a multiscale layered image fusion method to extract the most representative information for LSS-target detection. In addition, an improved self-balanced sensitivity segment model is proposed to detect the fused LSS target, which can further improve both the detection accuracy and the computational efficiency. We conduct extensive ablation studies to validate the efficacy of the proposed LSS-target detection method on three public datasets and three self-collected datasets. The superior performance over the state of the arts has fully demonstrated the efficacy of the proposed approach. |
| first_indexed | 2024-12-21T01:32:25Z |
| format | Article |
| id | doaj.art-2d78e2424337433395e949c0ddc8f912 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-21T01:32:25Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-2d78e2424337433395e949c0ddc8f9122022-12-21T19:20:19ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-01142971298310.1109/JSTARS.2021.30614969362227Fusion of Infrared and Visible Images for Remote Detection of Low-Altitude Slow-Speed Small TargetsHaijiang Sun0Qiaoyuan Liu1Jiacheng Wang2Jinchang Ren3https://orcid.org/0000-0001-6116-3194Yanfeng Wu4Huimin Zhao5https://orcid.org/0000-0002-6877-2002Huakang Li6Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, ChinaSchool of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou, China28th Research Institute of China Electronics Technology Group, Nanjing, ChinaSchool of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou, ChinaSchool of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou, ChinaDetection of the low-altitude and slow-speed small (LSS) targets is one of the most popular research topics in remote sensing. Despite of a few existing approaches, there is still an accuracy gap for satisfying the practical needs. As the LSS targets are too small to extract useful features, deep learning based algorithms can hardly be used. To this end, we propose in this article an effective strategy for determining the region of interest, using a multiscale layered image fusion method to extract the most representative information for LSS-target detection. In addition, an improved self-balanced sensitivity segment model is proposed to detect the fused LSS target, which can further improve both the detection accuracy and the computational efficiency. We conduct extensive ablation studies to validate the efficacy of the proposed LSS-target detection method on three public datasets and three self-collected datasets. The superior performance over the state of the arts has fully demonstrated the efficacy of the proposed approach.https://ieeexplore.ieee.org/document/9362227/Background subtractionimage fusionlow-altitude and slow-speed small (LSS) target detectionsaliency detection |
| spellingShingle | Haijiang Sun Qiaoyuan Liu Jiacheng Wang Jinchang Ren Yanfeng Wu Huimin Zhao Huakang Li Fusion of Infrared and Visible Images for Remote Detection of Low-Altitude Slow-Speed Small Targets IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Background subtraction image fusion low-altitude and slow-speed small (LSS) target detection saliency detection |
| title | Fusion of Infrared and Visible Images for Remote Detection of Low-Altitude Slow-Speed Small Targets |
| title_full | Fusion of Infrared and Visible Images for Remote Detection of Low-Altitude Slow-Speed Small Targets |
| title_fullStr | Fusion of Infrared and Visible Images for Remote Detection of Low-Altitude Slow-Speed Small Targets |
| title_full_unstemmed | Fusion of Infrared and Visible Images for Remote Detection of Low-Altitude Slow-Speed Small Targets |
| title_short | Fusion of Infrared and Visible Images for Remote Detection of Low-Altitude Slow-Speed Small Targets |
| title_sort | fusion of infrared and visible images for remote detection of low altitude slow speed small targets |
| topic | Background subtraction image fusion low-altitude and slow-speed small (LSS) target detection saliency detection |
| url | https://ieeexplore.ieee.org/document/9362227/ |
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