Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure
Infrared small target detection plays a crucial role in fields such as remote sensing and surveillance. However, during long-distance imaging, factors such as atmospheric attenuation lead to a low signal-to-clutter ratio for the targets, making their features difficult to extract effectively. Additi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/8/1442 |
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| author | Yuyang Xi Yushan Zhang Ying Jiang Liuwei Zhang Qingyu Hou |
| author_facet | Yuyang Xi Yushan Zhang Ying Jiang Liuwei Zhang Qingyu Hou |
| author_sort | Yuyang Xi |
| collection | DOAJ |
| description | Infrared small target detection plays a crucial role in fields such as remote sensing and surveillance. However, during long-distance imaging, factors such as atmospheric attenuation lead to a low signal-to-clutter ratio for the targets, making their features difficult to extract effectively. Additionally, in complex background environments, background components that resemble the target morphology highly interfere with detection tasks. Therefore, infrared weak small target detection in complex backgrounds faces challenges of low detection accuracy and high false alarm rates. To solve the above difficulties, a novel entropy variation weighted local contrast measure (EVWLCM) is proposed. Firstly, a target saliency enhancement method based on a family of generalized Gaussian functions is introduced, which accurately characterizes the grayscale distribution states of various targets in infrared images. Secondly, a novel adaptive weighting strategy based on local joint entropy variation characteristics is suggested. Specifically, the spatial grayscale distribution difference between the target and the background is effectively perceived, enhancing the target while suppressing the background. Finally, experimental results on real infrared images show that EVWLCM outperforms existing methods on both public and private datasets. Additionally, the average processing speed of EVWLCM is 34 frames per second, which meets the requirements for real-time scenarios. |
| format | Article |
| id | doaj-art-614bc1e23bf748e9838dc9884df0155f |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-614bc1e23bf748e9838dc9884df0155f2025-08-20T03:13:32ZengMDPI AGRemote Sensing2072-42922025-04-01178144210.3390/rs17081442Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast MeasureYuyang Xi0Yushan Zhang1Ying Jiang2Liuwei Zhang3Qingyu Hou4The Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaShanghai Institute of Satellite Engineering, Shanghai 201109, ChinaThe Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaThe Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaThe Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaInfrared small target detection plays a crucial role in fields such as remote sensing and surveillance. However, during long-distance imaging, factors such as atmospheric attenuation lead to a low signal-to-clutter ratio for the targets, making their features difficult to extract effectively. Additionally, in complex background environments, background components that resemble the target morphology highly interfere with detection tasks. Therefore, infrared weak small target detection in complex backgrounds faces challenges of low detection accuracy and high false alarm rates. To solve the above difficulties, a novel entropy variation weighted local contrast measure (EVWLCM) is proposed. Firstly, a target saliency enhancement method based on a family of generalized Gaussian functions is introduced, which accurately characterizes the grayscale distribution states of various targets in infrared images. Secondly, a novel adaptive weighting strategy based on local joint entropy variation characteristics is suggested. Specifically, the spatial grayscale distribution difference between the target and the background is effectively perceived, enhancing the target while suppressing the background. Finally, experimental results on real infrared images show that EVWLCM outperforms existing methods on both public and private datasets. Additionally, the average processing speed of EVWLCM is 34 frames per second, which meets the requirements for real-time scenarios.https://www.mdpi.com/2072-4292/17/8/1442infrared small target detectionlocal contrast measuregeneralized Gaussian function familylocal joint entropy variation characteristics |
| spellingShingle | Yuyang Xi Yushan Zhang Ying Jiang Liuwei Zhang Qingyu Hou Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure Remote Sensing infrared small target detection local contrast measure generalized Gaussian function family local joint entropy variation characteristics |
| title | Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure |
| title_full | Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure |
| title_fullStr | Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure |
| title_full_unstemmed | Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure |
| title_short | Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure |
| title_sort | infrared small target detection based on entropy variation weighted local contrast measure |
| topic | infrared small target detection local contrast measure generalized Gaussian function family local joint entropy variation characteristics |
| url | https://www.mdpi.com/2072-4292/17/8/1442 |
| work_keys_str_mv | AT yuyangxi infraredsmalltargetdetectionbasedonentropyvariationweightedlocalcontrastmeasure AT yushanzhang infraredsmalltargetdetectionbasedonentropyvariationweightedlocalcontrastmeasure AT yingjiang infraredsmalltargetdetectionbasedonentropyvariationweightedlocalcontrastmeasure AT liuweizhang infraredsmalltargetdetectionbasedonentropyvariationweightedlocalcontrastmeasure AT qingyuhou infraredsmalltargetdetectionbasedonentropyvariationweightedlocalcontrastmeasure |