基于时域局部空间熵与空域多尺度特征的红外小目标检测

李恒超; 刘艳琼; 尹加杰; 雷森

doi:10.3969/j.issn.0258-2724.20240225

基于时域局部空间熵与空域多尺度特征的红外小目标检测

doi: 10.3969/j.issn.0258-2724.20240225

西南交通大学信息科学与技术学院，四川成都 611756

基金项目: 国家自然科学基金项目（62271418）；四川省自然科学基金项目（2023NSFSC0030）；博士后创新人才支持计划（BX20240291）

详细信息

作者简介:
李恒超（1978—），男，教授，博士，研究方向为SAR图像统计建模、遥感图像处理与模式识别，E-mail：lihengchao_78@163.com

通讯作者:
雷森（1992—），男，讲师，博士，研究方向为计算机视觉、深度学习和遥感图像处理，E-mail：senlei@swjtu.edu.cn

中图分类号: TP391.41；TN219
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- 被引次数: 0
出版历程
- 收稿日期: 2024-05-09
- 修回日期: 2024-09-04
- 网络出版日期: 2025-10-18

Infrared Small Target Detection Based on Temporal Local Spatial Entropy and Spatial Multi-Scale Feature

School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China

摘要

摘要:
红外成像技术广泛应用于军事和民用领域，其中红外小目标检测作为应用中不可或缺的环节，具有重要的实际价值. 针对现有方法无法有效区分类目标稀疏结构与真实目标的问题，本文提出一种融合时域局部空间熵与空域多尺度特征的红外小目标检测算法. 在时域分支上首先设计基于图像块相似性度量的密度峰值聚类算法，定位红外小目标候选区域，减少对背景的冗余计算. 进一步地，提出一种基于帧间局部差异的时域局部空间熵，充分挖掘目标与背景熵值在局部区域的不同变化特性，解决类目标稀疏结构引起的虚警问题. 此外，引入空域多尺度特征提取分支，构建时空融合特征，降低候选区域定位中的漏检率，提高对不同尺度小目标的检测能力. 在5组不同场景的序列上与9种算法进行对比，本文所提出方法的BSF （background suppression factor）均优于其他方法的，在表现最好的序列5上其BSF值是次优方法的2.02倍，且在ROC （receiver operating characteristic curve）曲线中4组序列上表现为最优. 综上所述，相比于其他方法，所提出方法能够在类目标稀疏结构干扰下精准检出小目标.
- 红外小目标检测 /
- 密度峰值聚类 /
- 局部空间熵 /
- 多尺度特征 /
- 空时特征融合
Abstract:
Infrared imaging technology is widely used in military and civilian fields. As an indispensable part of the application, infrared small target detection has important practical significance. However, the existing methods hardly distinguish the real target from the target-like sparse structure. Therefore, an infrared small target detection algorithm was proposed by fusing the temporal local spatial entropy and the spatial multi-scale feature. In the temporal branch, a density peak clustering algorithm based on the similarity measurement of patches was designed to locate the candidate region of infrared small targets and reduce redundant calculation of backgrounds. Moreover, temporal local spatial entropy based on the local difference between frames was proposed to explore the variations in target and background entropy values in local regions and solve the false alarm caused by the target-like sparse structure. In addition, the spatial multi-scale feature branch was introduced to construct the spatio-temporal fusion feature, reducing the missed detection rate in the location of candidate regions and improving the detection ability of small targets at different scales. Compared with that of nine algorithms on five sets of sequences, the background suppression factor (BSF) of the proposed method is superior. The BSF is 2.02 times better than that of the suboptimal method on the best-performing sequence 5, and it is optimal on four sets of sequences in the receiver operating characteristic curve (ROC). In summary, compared with other methods, the proposed method can detect small targets accurately under the target-like sparse structure.
- infrared small target detection /
- density peak clustering /
- local spatial entropy /
- multi-scale feature /
- spatial-temporal feature fusion

HTML全文

图 1 基于时域局部空间熵与空域多尺度特征的红外小目标检测方法流程

Figure 1. Flowchart of proposed STFF method for infrared small target detection

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图 2 δ-ρ分布

Figure 2. δ−ρ distribution

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图 3 红外小目标局部灰度值分析

Figure 3. Local gray value analysis of infrared small targets

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图 4 红外图像和STFF结果

Figure 4. Infrared images and STFF results

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图 5 各序列的示例图像及三维曲面图

Figure 5. Representative images and 3D surface maps for each sequence

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图 6 不同序列下对比算法的ROC曲线

Figure 6. ROC curves for comparison algorithms on different sequences

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图 7 红外图像和STFF结果

Figure 7. Infrared images and STFF results

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表 1 各序列详细信息

Table 1. Description for each sequence

序列	帧	分辨率/ 像素	背景描述	目标特征
Seq 1	200	256 × 256	近距离、高亮块状背景	部分帧目标淹没
Seq 2	200	256 × 256	高亮块状背景、低对比度地面背景	目标由近及远
Seq 3	200	256 × 256	高亮度光纹、地空背景	扩展目标、目标机动
Seq 4	150	256 × 256	植被、地空背景	目标形状变化
Seq 5	100	256 × 256	高亮度建筑物、噪点干扰	高斯形状

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表 2 不同算法在5组序列上的定性对比结果

Table 2. Qualitative comparison results of different algorithms on five sets of sequences

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表 3 在5个序列上10种方法的BSF、SCRG结果

Table 3. BSF and SCRG results of 10 methods on five sequences

指标	方法	Seq 1	Seq 2	Seq 3	Seq 4	Seq 5
SCRG	MAXMEAN	0.1675	0.2504	0.2258	0.5809	18.9805
	WLCM	0.7712	0.8673	0.9183	1.2899	23.5724
	NRAM	17.0208	25.7585	28.6412	51.2311	133.5389
	STLCF	3.4151	3.4846	5.4057	0.5579	16.3252
	STLDM	3.4364	4.1492	11.4342	2.3862	111.5879
	ASTTV-NTLA	22.5991	29.4344	3.5312	2.9850	0.1765
	NSTSM	10.6381	17.2334	15.4689	1.0603	163.8064
	ACM	11.1839	6.9492	26.7686	33.8603	194.6835
	RDIAN	17.8196	17.0500	26.9992	44.4159	46.3680
	STFF	18.3526	28.2757	40.8815	68.7667	449.2500
BSF	MAXMEAN	1115.5920	916.1936	625.3778	442.7659	711.9334
	WLCM	715.7735	579.3265	556.9580	389.5317	506.5658
	NRAM	5492.4800	5730.4223	4212.5532	5112.0712	2721.1857
	STLCF	535.9361	493.4616	627.2045	364.0858	371.9604
	STLDM	2264.2647	2115.5049	3018.7691	1916.0918	1908.8198
	ASTTV-NTLA	5152.2584	5750.8548	333.0346	178.7372	77.6619
	NSTSM	1778.6651	2271.0224	2063.3393	1204.0717	2249.0059
	ACM	2113.3549	5966.8174	1967.2721	2062.1592	1860.3962
	RDIAN	2873.5632	2041.9871	2224.4897	3180.8297	866.3432
	STFF	7093.3875	7575.9045	6363.5806	7055.7701	5484.5020

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表 4 实时性能表现对比

Table 4. Real-time performance comparison s

序列	MAXMEAN	WLCM	NRAM	STLCF	STLDM	ASTTV-NTLA	NSTSM	ACM	RDIAN	STFF
Seq 1	0.0113	1.6204	0.3306	23.3607	10.7320	1.8871	0.2020	0.0334	0.1110	1.6258
Seq 2	0.0033	1.5935	0.3132	23.7134	10.5746	1.8757	0.1904	0.0323	0.1119	1.6127
Seq 3	0.0164	1.6091	0.2755	22.5361	10.2368	1.7116	0.1770	0.0320	0.1064	1.6838
Seq 4	0.0163	1.5866	0.3026	21.5019	10.1293	1.8103	0.1821	0.0394	0.1101	1.6064
Seq 5	0.0159	1.6025	0.3615	21.9445	10.2612	1.7122	0.1767	0.0536	0.1246	1.5978

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表 5 消融实验结果

Table 5. Experimental results of ablation

模块	SCRG	BSF
TLSE	2.6330	1995.5313
TLSE+DPC4RP	5.4320	4051.9170
TLSE+SMSF	13.2874	4301.1759
TLSE+DPC4RP+SMSF	18.3526	7093.3875

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