Structured robust principal component analysis for infrared small target detection

doi:10.23919/JSEE.2026.000093

Abstract

Abstract:

Extracting infrared small targets from heterogeneous backgrounds remains a challenging task, as these targets lack salient texture and morphological features while the backgrounds are cluttered with noise. Therefore, effectively extracting discriminative features is essential for achieving complete and accurate detection. To address this issue, this paper proposes an algorithmic framework based on robust principal component analysis (RPCA), specifically designed for infrared small target detection in complex backgrounds. First, infrared small target detection is formulated as a generalized RPCA problem. A discriminative and reconstructive dictionary is constructed using supervised learning. Next, by introducing an ideal regularization term, the infrared image is reconstructed without losing structural information, yielding a discriminative principal component representation with respect to the learned dictionary. Finally, the structural features of infrared small targets are reconstructed via sparse coding, thereby enabling the extraction of infrared small targets. Extensive experimental results demonstrate the effectiveness of the proposed method.

Key words: infrared small target detection, heterogeneous background, robust principal component analysis, sparse coding

Yongqiang ZHANG, Yongji LI, Meng CAI, Ye ZHANG, Yong TAN. Structured robust principal component analysis for infrared small target detection[J]. Journal of Systems Engineering and Electronics, 2026, 37(3): 779-787.

Figures/Tables 4

References 36

1	HADHOUD M M, THOMAS D W The two-dimensional adaptive LMS (TDLMS) algorithm. IEEE Trans. on Circuits and Systems, 1988, 35 (5): 485- 494. doi: 10.1109/31.1775
2	SONI T, ZEIDLER J R, KU W H Performance evaluation of 2-D adaptive prediction filters for detection of small objects in image data. IEEE Trans. on Image Processing, 1993, 2 (3): 327- 340. doi: 10.1109/83.236534
3	HAN J H, YONG M, BO Z, et al A robust infrared small target detection algorithm based on human visual system. IEEE Geoscience and Remote Sensing Letters, 2014, 11 (12): 2168- 2172. doi: 10.1109/LGRS.2014.2323236
4	WEI Y T, YOU X G, LI H Multiscale patch-based contrast measure for small infrared target detection. Pattern Recognition, 2016, 58, 216- 226. doi: 10.1016/j.patcog.2016.04.002
5	LI Y J, WANG L P, CHEN S C Smile: spatial-spectral mamba interactive learning for infrared small target detection. IEEE Trans. on Geoscience and Remote Sensing, 2025, 63, 5005214.
6	TONG Y F, LIU J, FU Z L, et al Guided attention and joint loss for infrared dim small target detection. IEEE Trans. on Geoscience and Remote Sensing, 2024, 62, 4110914. doi: 10.1109/tgrs.2024.3477575/v2/review1
7	AIBIBU T, LAN J H, ZENG Y L, et al An efficient rep-style Gaussian-Wasserstein network: improved UAV infrared small object detection for urban road surveillance and safety. Remote Sensing, 2023, 16 (1): 25. doi: 10.3390/rs16010025
8	GIRSHICK R. Fast R-CNN. Proc. of the IEEE International Conference on Computer Vision, 2015: 1440−1448.
9	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection. Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779−788.
10	CHEN Z H, WU G Y, GAO H M, et al Local aggregation and global attention network for hyperspectral image classification with spectral-induced aligned superpixel segmentation. Expert Systems with Applications, 2023, 232, 120828. doi: 10.1016/j.eswa.2023.120828
11	YANG B, CHENG X W, CHEN W, et al A graph-based hyperspectral change detection framework using difference augmentation and progressive reconstruction with limited labels. IEEE Trans. on Geoscience and Remote Sensing, 2024, 62, 5518914. doi: 10.1109/tgrs.2024.3403237
12	KONG X, YANG C P, CAO S Y, et al Infrared small target detection via nonconvex tensor fibered rank approximation. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5000321. doi: 10.1109/tgrs.2021.3068465
13	JI S, ZHANG H F, ZHANG J G, et al A three-stage model for infrared small target detection with spatial and semantic feature fusion. Expert Systems with Applications, 2025, 295, 128776.
14	LI Y J, WANG L P, CHEN S C From optimization to network: a low-rank and sparse-aware deep unfolding framework for infrared small target detection. Advanced Engineering Informatics, 2026, 69, 103991.
15	LI Y J, WANG L P, CHEN S C Frequency-spatial interaction reinforcement paradigm for infrared small target detection. IEEE Trans. on Geoscience and Remote Sensing, 2025, 63, 5008915. doi: 10.1109/JSTARS.2025.3548314
16	YING X Y, LIU L, WANG Y Q, et al. Mapping degeneration meets label evolution: learning infrared small target detection with single point supervision. Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 15528−15538.
17	ZHANG M J, ZHANG R, YANG Y X, et al. ISNet: shape matters for infrared small target detection. Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 867−876.
18	NIAN B K, JIANG B, SHI H J, et al Local contrast attention guide network for detecting infrared small targets. IEEE Trans. on Geoscience and Remote Sensing, 2023, 61, 5607513. doi: 10.1109/tgrs.2023.3266447
19	ZHANG M J, BAI H C, ZHANG J, et al. RKformer: Runge-Kutta transformer with random-connection attention for infrared small target detection. Proc. of the 30th ACM International Conference on Multimedia, 2022: 1730−1738.
20	ZHANG M J, YANG H D, GUO J, et al IRPruneDet: efficient infrared small target detection via wavelet structure-regularized soft channel pruning. Proc. of the AAAI Conference on Artificial Intelligence, 2024, 38 (7): 7224- 7232. doi: 10.1609/aaai.v38i7.28551
21	WU T H, LI B Y, LUO Y H, et al MTU-Net: multilevel transunet for space-based infrared tiny ship detection. IEEE Trans. on Geoscience and Remote Sensing, 2023, 61, 5601015.
22	ZHANG M J, WANG Y C, GUO J, et al. IRSAM: advancing segment anything model for infrared small target detection. Proc. of the 18th European Conference on Computer Vision, 2024: 233−249.
23	ZHANG M J, ZHANG R, ZHANG J, et al. Dim2Clear network for infrared small target detection. IEEE Trans. on Geoscience and Remote Sensing, 2023, 61: 5626015.
24	LIU Q K, LIU R, ZHENG B L, et al. Infrared small target detection with scale and location sensitivity. Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024: 17490−17499.
25	CHOI L, CHUNG W Y, PARK C G CSI-Net: CNN Swin transformer integrated network for infrared small target detection. International Journal of Control, Automation and Systems, 2024, 22 (9): 2899- 2908. doi: 10.1007/s12555-024-0089-8
26	LIU P, LUO Y S, WANG W Z, et al. Motion-enhanced nonlocal similarity implicit neural representation for infrared dim and small target detection. https://arxiv.org/abs/2504.15665.
27	JIA G M, CHENG Y, CHEN T IRGraphSeg: infrared small target detection based on hierarchical GNN. IEEE Geoscience and Remote Sensing Letters, 2024, 21, 6005505. doi: 10.1109/lgrs.2024.3374431
28	XIA C Q, CHEN S H, HUANG R H, et al Separable spatial-temporal patch-tensor pair completion for infrared small target detection. IEEE Trans. on Geoscience and Remote Sensing, 2024, 62, 5001620.
29	MA F, QU Q, YANG F X, et al Hankel tensor subspace representation for remotely sensed image fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18, 8630- 8642.
30	LIU J, MUSIALSKI P, WONKA P, et al Tensor completion for estimating missing values in visual data. TIEEE Trans. on Pattern Analysis and Machine Intelligence, 2013, 35 (1): 208- 220.
31	MU C, HUANG B, WRIGHT J, et al. Square deal: lower bounds and improved relaxations for tensor recovery. Proc. of the 31st International Conference on Machine Learning, 2014, 32(2): 73−81.
32	SALAKHUTDINOV R, SREBRO N. Collaborative filtering in a non-uniform world: learning with the weighted trace norm. Proc. of the 24th International Conference on Neural Information Processing Systems, 2010: 2056−2064.
33	SHEN Y W, LI Q W, XU C, et al Graph-based context learning network for infrared small target detection. Neurocomputing, 2025, 616, 128949. doi: 10.1016/j.neucom.2024.128949
34	YUAN S, QIN H L, YAN X, et al SCTransNet: spatial-channel cross transformer network for infrared small target detection. IEEE Trans. on Geoscience and Remote Sensing, 2024, 62, 5002615.
35	LI B Y, XIAO C, WANG L G, et al Dense nested attention network for infrared small target detection. IEEE Trans. on Image Processing, 2022, 32, 1745- 1758.
36	ZHANG T Y, LI L, CAO S Y, et al Attention-guided pyramid context networks for detecting infrared small target under complex background. IEEE Trans. on Aerospace and Electronic Systems, 2023, 59 (4): 4250- 4261. doi: 10.1109/TAES.2023.3238703

Algorithm	IRSTD-1k
Algorithm	IOU	nIOU	$ {F}_{1} $	$ {P}_{{\mathrm{d}}} $	$ {F}_{{\mathrm{a}}} $
GCLNet [33]	81.45	82.31	91.32	97.26	17.26
SCTransNet [34]	80.26	80.78	87.16	90.25	13.25
DNA-Net [35]	76.33	77.51	83.10	91.64	31.00
AGPCNet [36]	63.25	65.07	76.81	95.20	36.25
Proposed method	81.56	82.30	92.56	98.21	17.77

[1]	Hailong ZHANG, Gong ZHANG, Biao XUE, Jiawen YUAN. Mainlobe jamming suppression via improved BSS method for rotated array radar [J]. Journal of Systems Engineering and Electronics, 2022, 33(6): 1151-1158.
[2]	Liangkui LIN, Shaoyou WANG, Zhongxing TANG. Using deep learning to detect small targets in infrared oversampling images [J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 947-952.
[3]	Ning Tang, Xunzhang Gao, and Xiang Li. ISAR target recognition based on non-negative sparse coding [J]. Journal of Systems Engineering and Electronics, 2012, 23(6): 849-857.
[4]	Xiujie Qu, He Chen, and Guihua Peng. Novel detection method for infrared small targets using weighted information entropy [J]. Journal of Systems Engineering and Electronics, 2012, 23(6): 838-842.