Generative Adversarial Differential Analysis for Infrared Small Target Detection
Infrared small target detection refers to the extraction of small targets in a complex, low signal-to-noise ratio background. Depthwise convolution makes it difficult to comprehensively characterize small infrared targets and ignores the importance of image background for the detection task. In this...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10460986/ |
| _version_ | 1797243314296061952 |
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| author | Zongfang Ma Shuo Pang Fan Hao |
| author_facet | Zongfang Ma Shuo Pang Fan Hao |
| author_sort | Zongfang Ma |
| collection | DOAJ |
| description | Infrared small target detection refers to the extraction of small targets in a complex, low signal-to-noise ratio background. Depthwise convolution makes it difficult to comprehensively characterize small infrared targets and ignores the importance of image background for the detection task. In this article, we propose the generative adversarial differential analysis (GADA) model for infrared small target detection, the core of which aims to weaken the reliance on target features and enhance the use of background information. Specifically, we first construct pseudobackground labels by the fast marching method. Then, the background-guided generative adversarial network is used to learn the background data distribution. On this basis, the differential image containing interest regions of small targets is obtained by differential analysis. Finally, the detection results are obtained by performing an elaborate characterization of the interest regions. The effectiveness of GADA is verified with three public datasets. Compared to several state-of-the-art methods, GADA achieves better performance in terms of <inline-formula><tex-math notation="LaTeX">$F1$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$IoU$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$AUC$</tex-math></inline-formula>. |
| first_indexed | 2024-04-24T18:53:09Z |
| format | Article |
| id | doaj.art-a052094a287240c0ae42a0e4df787f60 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-24T18:53:09Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-a052094a287240c0ae42a0e4df787f602024-03-26T17:48:03ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-01176616662610.1109/JSTARS.2024.337405410460986Generative Adversarial Differential Analysis for Infrared Small Target DetectionZongfang Ma0https://orcid.org/0000-0002-0942-9052Shuo Pang1https://orcid.org/0009-0006-0133-0804Fan Hao2https://orcid.org/0000-0002-5598-5500School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, ChinaSchool of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, ChinaSchool of Integrated Circuits, Beijing University of Posts and Telecommunications, Beijing, ChinaInfrared small target detection refers to the extraction of small targets in a complex, low signal-to-noise ratio background. Depthwise convolution makes it difficult to comprehensively characterize small infrared targets and ignores the importance of image background for the detection task. In this article, we propose the generative adversarial differential analysis (GADA) model for infrared small target detection, the core of which aims to weaken the reliance on target features and enhance the use of background information. Specifically, we first construct pseudobackground labels by the fast marching method. Then, the background-guided generative adversarial network is used to learn the background data distribution. On this basis, the differential image containing interest regions of small targets is obtained by differential analysis. Finally, the detection results are obtained by performing an elaborate characterization of the interest regions. The effectiveness of GADA is verified with three public datasets. Compared to several state-of-the-art methods, GADA achieves better performance in terms of <inline-formula><tex-math notation="LaTeX">$F1$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$IoU$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$AUC$</tex-math></inline-formula>.https://ieeexplore.ieee.org/document/10460986/Change detectiongenerative adversarial network (GAN)image segmentationinfrared small target detection |
| spellingShingle | Zongfang Ma Shuo Pang Fan Hao Generative Adversarial Differential Analysis for Infrared Small Target Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change detection generative adversarial network (GAN) image segmentation infrared small target detection |
| title | Generative Adversarial Differential Analysis for Infrared Small Target Detection |
| title_full | Generative Adversarial Differential Analysis for Infrared Small Target Detection |
| title_fullStr | Generative Adversarial Differential Analysis for Infrared Small Target Detection |
| title_full_unstemmed | Generative Adversarial Differential Analysis for Infrared Small Target Detection |
| title_short | Generative Adversarial Differential Analysis for Infrared Small Target Detection |
| title_sort | generative adversarial differential analysis for infrared small target detection |
| topic | Change detection generative adversarial network (GAN) image segmentation infrared small target detection |
| url | https://ieeexplore.ieee.org/document/10460986/ |
| work_keys_str_mv | AT zongfangma generativeadversarialdifferentialanalysisforinfraredsmalltargetdetection AT shuopang generativeadversarialdifferentialanalysisforinfraredsmalltargetdetection AT fanhao generativeadversarialdifferentialanalysisforinfraredsmalltargetdetection |