Zixu Huang;Erwei Zhao;Wei Zheng;Xiaodong Peng;Wenlong Niu;Zhen Yang
{"title":"通过两阶段特征互补改进张量低阶稀疏分解进行红外小目标探测","authors":"Zixu Huang;Erwei Zhao;Wei Zheng;Xiaodong Peng;Wenlong Niu;Zhen Yang","doi":"10.1109/JSTARS.2024.3463017","DOIUrl":null,"url":null,"abstract":"Infrared small target detection has been widely used in military and civil fields. However, due to the insufficient feature integration capabilities of existing methods, effectively separating strong background clutter and targets in complex scenes remains difficult. To address this issue, we propose a two-stage feature complementary improved tensor low-rank sparse decomposition (TLRSD) method. The detection process is divided into two stages: tensor initialization and tensor decomposition, effectively integrating local and nonlocal features. In the tensor initialization stage, inspired by the local saliency of the target and the local consistency of the background, we design a three-layer directional filtering (TLDF) operator for preliminary clutter suppression and target enhancement. Then, to promote the complementary advantages of local and nonlocal features, we refer to the TLDF and the original image to provide a targeted initialization strategy for the TLRSD model. In the tensor decomposition stage, we develop a robust partial sum of the tubal nuclear norm as a nonconvex approximation of tensor rank, which can adaptively adjust the singular value distribution, thus adapting to diversity scenes. Meanwhile, we finely adjust the balance between low-rank and sparse components in the model-solving process through a nonlinear reweighting strategy, accelerating the optimization convergence speed and improving the model's background recovery ability. Extensive experiments on five practical datasets demonstrate that the proposed method is more effective and robust compared to ten state-of-the-art approaches.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10682794","citationCount":"0","resultStr":"{\"title\":\"Infrared Small Target Detection via Two-Stage Feature Complementary Improved Tensor Low-Rank Sparse Decomposition\",\"authors\":\"Zixu Huang;Erwei Zhao;Wei Zheng;Xiaodong Peng;Wenlong Niu;Zhen Yang\",\"doi\":\"10.1109/JSTARS.2024.3463017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Infrared small target detection has been widely used in military and civil fields. However, due to the insufficient feature integration capabilities of existing methods, effectively separating strong background clutter and targets in complex scenes remains difficult. To address this issue, we propose a two-stage feature complementary improved tensor low-rank sparse decomposition (TLRSD) method. The detection process is divided into two stages: tensor initialization and tensor decomposition, effectively integrating local and nonlocal features. In the tensor initialization stage, inspired by the local saliency of the target and the local consistency of the background, we design a three-layer directional filtering (TLDF) operator for preliminary clutter suppression and target enhancement. Then, to promote the complementary advantages of local and nonlocal features, we refer to the TLDF and the original image to provide a targeted initialization strategy for the TLRSD model. In the tensor decomposition stage, we develop a robust partial sum of the tubal nuclear norm as a nonconvex approximation of tensor rank, which can adaptively adjust the singular value distribution, thus adapting to diversity scenes. Meanwhile, we finely adjust the balance between low-rank and sparse components in the model-solving process through a nonlinear reweighting strategy, accelerating the optimization convergence speed and improving the model's background recovery ability. 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Infrared Small Target Detection via Two-Stage Feature Complementary Improved Tensor Low-Rank Sparse Decomposition
Infrared small target detection has been widely used in military and civil fields. However, due to the insufficient feature integration capabilities of existing methods, effectively separating strong background clutter and targets in complex scenes remains difficult. To address this issue, we propose a two-stage feature complementary improved tensor low-rank sparse decomposition (TLRSD) method. The detection process is divided into two stages: tensor initialization and tensor decomposition, effectively integrating local and nonlocal features. In the tensor initialization stage, inspired by the local saliency of the target and the local consistency of the background, we design a three-layer directional filtering (TLDF) operator for preliminary clutter suppression and target enhancement. Then, to promote the complementary advantages of local and nonlocal features, we refer to the TLDF and the original image to provide a targeted initialization strategy for the TLRSD model. In the tensor decomposition stage, we develop a robust partial sum of the tubal nuclear norm as a nonconvex approximation of tensor rank, which can adaptively adjust the singular value distribution, thus adapting to diversity scenes. Meanwhile, we finely adjust the balance between low-rank and sparse components in the model-solving process through a nonlinear reweighting strategy, accelerating the optimization convergence speed and improving the model's background recovery ability. Extensive experiments on five practical datasets demonstrate that the proposed method is more effective and robust compared to ten state-of-the-art approaches.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.