首页|Infrared Small Target Detection Based on Prior Guided Dense Nested Network

Infrared Small Target Detection Based on Prior Guided Dense Nested Network

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  • NETL
  • NSTL
  • IEEE
Infrared small target detection (IRSTD) has been widely applied and developed in military and civilian fields, playing a vital role. Despite the extensive research foundation of traditional manual feature-based methods, they are still constrained by the inherent problem of infrared small targets lacking prior features. In recent years, the advancement of deep learning methods has enriched the research landscape in this field, yet they are still constrained by the imbalance of positive and negative samples between the target and the background. To address these issues, we propose a novel prior guided dense nested network (PGDN-Net), which ingeniously integrates traditional manual features with a deep learning network model. First, three prior features are extracted, including the high-order Riesz transform feature, the compactness and heterogeneity feature (CH), and the corner feature of the structure tensor (ST). Then, these features are input into a dense nested network for guidance, supported by a two-orientation attention aggregation module and a channel and spatial attention module. Different features play their respective guiding roles in different depths of the network. Through multiple attention mechanisms and feature fusion operations on the interested target area, the extraction and preservation of target features can be improved, while easily removing irrelevant backgrounds. Experiments on public datasets demonstrate the effectiveness and progressiveness of our PGDN-Net. Compared with other state-of-the-art methods, it achieves better performance in background suppression, target enhancement, probability of detection, and false alarm rate. In addition, the PGDN-Net model can effectively maintain and restore the original shape of the target while performing robust detection, which is beneficial for subsequent fine-grained recognition tasks.

Feature extractionDeep learningData miningFilteringTransformsTensorsObject detectionSparse matricesShapeMatrix decomposition

Chang Liu、Xuedong Song、Dianyu Yu、Linwei Qiu、Fengying Xie、Yue Zi、Zhenwei Shi

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Tianmushan Laboratory, Hangzhou, China|Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing, China|Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Shanghai Institute of Satellite Engineering, Shanghai, China

Tianmushan Laboratory, Hangzhou, China|Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing, China

School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China

Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing, China

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2025

10.1109/TGRS.2025.3542232

IEEE transactions on geoscience and remote sensing: Institute of Electrical and Electronics Engineers transactions on geoscience and remote sensing

IEEE transactions on geoscience and remote sensing: Institute of Electrical and Electronics Engineers transactions on geoscience and remote sensing

ISSN：
年,卷(期)：2025.63(1)
  • 69