Small Infrared Target Detection Based on Weighted Local Difference Measure

By He Deng, Xianping Sun, Maili Liu, Chaohui Ye, Xin Zhou. IEEE Transactions on Geoscience and Remote Sensing, 2016

365 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a86ae6382c7b22d20050a6399ad6c98ef984b6db

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. Landan Zhang
3. Kobus Barnard
4. Fei Zhou
5. Wei An
6. Yimian Dai
7. Yiquan Wu
8. Jie-Ru Guo

In Electrical and Electronic Engineering

1. Zhenming Peng
2. Kobus Barnard
3. Fei Zhou
4. Landan Zhang
5. Yimian Dai
6. Yiquan Wu
7. Wei An
8. Lei Wang

In Mechanics of Materials

1. Zhenming Peng
2. Yimian Dai
3. Bo Zhou
4. Yiquan Wu

Citations in the corpus

• Miss Detection vs. False Alarm: Adversarial Learning for Small Object Segmentation in Infrared Images (2019, methodology): “It learns to identify targets from complex background with fewer FAs, showing effectiveness in enhancing the targets compared with the contrast and saliency based methods such as LCM [4] and WLDM [9].”
• ISNet: Shape Matters for Infrared Small Target Detection (2022, background): “Infrared small target detection (IRSTD) has a wide range of important applications such as traffic management and marine rescue [8, 33, 37].”
• Infrared Small Target Detection Utilizing the Multiscale Relative Local Contrast Measure (2018, methodology): “It can be seen that DoG, ILCM, NLCM, and WLDM could not detect the small targets correctly for all the three sequences; instead, a lot of false alarms emerge.”
• Attentional Local Contrast Networks for Infrared Small Target Detection (2020, background): “com/YimianDai/open-alcnet to enhance the target and suppress the background clutters [9].”
• Asymmetric Contextual Modulation for Infrared Small Target Detection (2020, background): “, a salient outlier [3, 8], a sparse outlier in a lowrank background [5, 40], a pop-out outlier in smooth background [33, 7].”
• Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection (2017, methodology): “MPCM and WLDM failed to achieve good results because they suffered from a phenomenon we named rare structure effect which is caused by the inaccuracy of the local dissimilarity measure and often happens when the target is extremely dim.”
• Dense Nested Attention Network for Infrared Small Target Detection (2021, background): “surveillance [1], [2], early warning systems [3], [4], and”
• Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm (2019, background): “…of Gaussian (DoG) filter [30], local contrast measure (LCM) [16], relative local contrast measure (RLCM) [19], multiscale patch-based contrast measure (MPCM) [31], weighted local difference measure (WLDM) [32], and multiscale gray and variance difference (MGVD) [33] measure were raised gradually.”