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学者姓名：裘兆炳
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Abstract ：
Infrared small target detection is critical to infrared search and tracking systems. However, accurate and robust detection remains challenging due to the scarcity of target information and the complexity of clutter interference. Existing methods have some limitations in feature representation, leading to poor detection performance in complex scenes. Especially when there are sharp edges near the target or in cluster multitarget detection, the "target suppression" phenomenon tends to occur. To address this issue, we propose a robust unsupervised multifeature representation (RUMFR) method for infrared small target detection. On the one hand, robust unsupervised spatial clustering (RUSC) is designed to improve the accuracy of feature extraction; on the other hand, pixel-level multiple feature representation is proposed to fully utilize the target detail information. Specifically, we first propose the center-weighted interclass difference measure (CWIDM) with a trilayer design for fast candidate target extraction. Note that CWIDM also guides the parameter settings of RUSC. Then, the RUSC-based model is constructed to accurately extract target features in complex scenes. By designing the parameter adaptive strategy and iterative clustering strategy, RUSC can robustly segment cluster multitargets from complex backgrounds. Finally, RUMFR that fuses pixel-level contrast, distribution, and directional gradient features is proposed for better target representation and clutter suppression. Extensive experimental results show that our method has stronger feature representation capability and achieves better detection performance than several state-of-the-art methods.
Keyword ：
Clutter Clutter Feature extraction Feature extraction Fuses Fuses Image edge detection Image edge detection Infrared small target detection Infrared small target detection Noise Noise Object detection Object detection pixel-level multifeature representation pixel-level multifeature representation robust unsupervised spatial clustering (RUSC) robust unsupervised spatial clustering (RUSC) Sparse matrices Sparse matrices "target suppression" phenomenon "target suppression" phenomenon
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| GB/T 7714 | Chen, Liqiong , Wu, Tong , Zheng, Shuyuan et al. Robust Unsupervised Multifeature Representation for Infrared Small Target Detection [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2024 , 17 : 10306-10323 . |
| MLA | Chen, Liqiong et al. "Robust Unsupervised Multifeature Representation for Infrared Small Target Detection" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17 (2024) : 10306-10323 . |
| APA | Chen, Liqiong , Wu, Tong , Zheng, Shuyuan , Qiu, Zhaobing , Huang, Feng . Robust Unsupervised Multifeature Representation for Infrared Small Target Detection . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2024 , 17 , 10306-10323 . |
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Abstract ：
Infrared (IR) small target detection exerts a significant role in IR early warning and UAV surveillance. However, in the low-altitude slow-speed small (LSS) target detection scene, the existing algorithms cannot effectively suppress high-contrast corners and sparse edges in the low-altitude background, resulting in many false alarms. To solve this problem, we propose an IR LSS target detection method based on fusion of target sparsity and motion saliency (TSMS). In the low-rank sparse model, we introduce a robust dual-window gradient operator to construct a fine local prior, which avoids the influence of highlighted edges and corners; The Geman norm is used to approximate the background rank to accurately estimate the background and effectively extract sparse targets. Then, a motion saliency model based on inter-frame local matching is constructed to accurately extract the inter-frame features of small target. Finally, the real LSS target is obtained by fusing target sparsity and motion saliency. Experiments indicate that, compared with existing advanced methods, the proposed method has stronger robustness and can effectively detect LSS targets under complex low-altitude background. © 2024 Elsevier B.V.
Keyword ：
Infrared (IR) image Infrared (IR) image Low-rank sparse Low-rank sparse Motion saliency Motion saliency Prior weight Prior weight Small target detection Small target detection
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| GB/T 7714 | Wu, L. , Ma, Y. , Huang, J. et al. Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency [J]. | Infrared Physics and Technology , 2024 , 142 . |
| MLA | Wu, L. et al. "Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency" . | Infrared Physics and Technology 142 (2024) . |
| APA | Wu, L. , Ma, Y. , Huang, J. , Qiu, Z. , Fan, F. . Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency . | Infrared Physics and Technology , 2024 , 142 . |
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Abstract ：
With the development of modern weapons such as UAV swarms and multiwarhead missiles, infrared (IR) cluster small target detection technology has become increasingly important. However, the difficulty in characterizing cluster multitargets leads to poor detection performance of existing methods. On the one hand, this letter proposes improved DBSCAN (IDBSCAN) to accurately extract the features of cluster multitargets with unknown numbers and distribution. On the other hand, an IDBSCAN-based difference measure (IDBSCAN-DM) is proposed, which fuses saliency and distribution features to further enhance cluster multitargets. Specifically, we first design the multiscale sliding window to quickly extract candidate targets. Then, the IDBSCAN-based local window is constructed and IDBSCAN-DM is computed for better target enhancement and background suppression. Finally, adaptive threshold segmentation is performed on the IDBSCAN-DM map to detect real targets. Extensive comparative experiments demonstrate that the proposed method achieves better target enhancement and a higher probability of detection.
Keyword ：
Feature extraction Feature extraction Geoscience and remote sensing Geoscience and remote sensing Gray-scale Gray-scale IDBSCAN-based difference measure (IDBSCAN-DM) IDBSCAN-based difference measure (IDBSCAN-DM) Image segmentation Image segmentation improved DBSCAN (IDBSCAN) improved DBSCAN (IDBSCAN) infrared (IR) cluster small target infrared (IR) cluster small target Object detection Object detection Robustness Robustness Shape Shape
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| GB/T 7714 | Qiu, Zhaobing , Ma, Yong , Fan, Fan et al. Improved DBSCAN for Infrared Cluster Small Target Detection [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2023 , 20 . |
| MLA | Qiu, Zhaobing et al. "Improved DBSCAN for Infrared Cluster Small Target Detection" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 20 (2023) . |
| APA | Qiu, Zhaobing , Ma, Yong , Fan, Fan , Huang, Jun , Wu, Lang , Du, You . Improved DBSCAN for Infrared Cluster Small Target Detection . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2023 , 20 . |
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