Infrared Dim and Small Target Detection via Multiple Subspace Learning and Spatial-Temporal Patch-Tensor Model

By Yang Sun, Jungang Yang, Wei An. IEEE Transactions on Geoscience and Remote Sensing, 2021

306 citations recorded by the source corpus.

Robust detection of infrared small and dim targets with highly heterogeneous backgrounds plays an indispensable role in infrared search and tracking (IRST) system, which is still a challenging problem. In this article, a novel method based on multisubspace learning and spatial-temporal tensor data structure is aimed to solve this problem. First, a tensor data structure is constructed to use inner correlation in spatial and temporal domain of the infrared image sequence. Second, in consideration of the complex and heterogeneous backgrounds in infrared images, the proposed method promotes the multisubspace property to tensor domain to separate the target and the background more accurately. Finally, an efficient and effective optimization algorithm based on alternating direction method of multipliers (ADMM) is designed to solve this problem. Experimental results on various and real scenes demonstrate the superiority of the proposed method compared to other five baseline methods.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/78983aa8ce03156e05fe4e88d1056cbec8a7463b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Jie-Ru Guo
2. Yulan Guo
3. Chao Xiao
4. Longguang Wang
5. Miao Li
6. Rui Zhang
7. Jing Zhang
8. Boyang Li

In Electrical and Electronic Engineering

1. Jie-Ru Guo
2. Rui Zhang
3. Jing Zhang
4. Haicheng Bai
5. Mingjin Zhang
6. Wei An
7. Yuxiang Yang

Citations in the corpus

• ISNet: Shape Matters for Infrared Small Target Detection (2022, methodology): “For traditional methods, we choose Top-Hat [2], Max-Median [9], WSLCM [12], TLLCM [4], IPI [10], NRAM [38], RIPT [5], PSTNN [39], and MSLSTIPT [32].”
• Dense Nested Attention Network for Infrared Small Target Detection (2021, methodology): “To demonstrate the superiority of our method, we compare our DNA-Net to several state-of-the-art (SOTA) methods, including traditional methods (Top-Hat [6], Max-Median [9], WSLCM [13], TLLCM [12], IPI [7], NRAM [16], RIPT [8], PSTNN [17], MSLSTIPT [5]) and CNN-based methods (MDvsFA-cGAN [32], ACM [2”