Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

By Landan Zhang, Zhenming Peng. Remote Sensing, 2019

661 citations recorded by the source corpus.

Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e9ecdd3241b5c9cc27d1c1ac72a6724da8361431

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. Zhenming Peng
2. Jocelyn Chanussot
3. Danfeng Hong
4. Xin Wu
5. Kobus Barnard
6. Fei Zhou
7. Wei An
8. Siying Cao

In Electrical and Electronic Engineering

1. Zhenming Peng
2. Kobus Barnard
3. Fei Zhou
4. Wei An
5. Yimian Dai
6. Yiquan Wu
7. Jocelyn Chanussot
8. Danfeng Hong

Citations in the corpus

• Asymmetric Contextual Modulation for Infrared Small Target Detection (2020, methodology): “Modulation Scheme FPN as Host Network U-Net as Host Network IoU nIoU IoU nIoU b = 1 b = 2 b = 3 b = 4 b = 1 b = 2 b = 3 b = 4 b = 1 b = 2 b = 3 b = 4 b = 1 b = 2 b = 3 b = 4 TopDownLocal 0.595 0.648 0.693 0.713 0.635 0.662 0.688 0.703 0.648 0.710 0.713 0.718 0.673 0.692 0.694 0.697 BiGlobal 0.599 0.”
• 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”
• ISNet: Shape Matters for Infrared Small Target Detection (2022, methodology): “Representative methods include HVS based methods, such as tri-layer local contrast measure (TLLCM) [4] and weighted strengthened local contrast measure (WSLCM) [12], filtering based methods [2, 9], as well as low-rank based methods, such as reweighted infrared patch-tensor (RIPT) [5], partial sum of”
• Attention-Guided Pyramid Context Networks for Detecting Infrared Small Target Under Complex Background (2023, methodology): “…method multiscale patch-based contrast measure (MPCM) [10], matrix-optimization method non-convex rank approximation minimization (NRAM) [16], multisubspace optimized self-regularized weighted sparse (SRWS) [18], and tensor-optimization partial sum of the tensor nuclear norm (PSTNN) [36].”
• UIU-Net: U-Net in U-Net for Infrared Small Object Detection (2022, background): “Table III lists the detection accuracy of nine infrared detection methods, including infrared patch image (IPI) [7], local contrast measure (LCM) [8], reweighted infrared patch-tensor (RIPT) [14], multiscale gray difference weighted image entropy (MGDWIE) [11], nonnegativity-constrained variational ”