Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure.
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| Title: | Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure. |
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| Authors: | Xi, Yuyang1 (AUTHOR), Zhang, Yushan2 (AUTHOR), Jiang, Ying1,3 (AUTHOR), Zhang, Liuwei1 (AUTHOR), Hou, Qingyu1,2,3 (AUTHOR) wonder.xl@hit.edu.cn |
| Source: | Remote Sensing. Apr2025, Vol. 17 Issue 8, p1442. 22p. |
| Subjects: | Gaussian function, Infrared imaging, Grayscale model, Remote sensing, False alarms |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 184759244 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xi%2C+Yuyang%22">Xi, Yuyang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yushan%22">Zhang, Yushan</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Ying%22">Jiang, Ying</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Liuwei%22">Zhang, Liuwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hou%2C+Qingyu%22">Hou, Qingyu</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> wonder.xl@hit.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Apr2025, Vol. 17 Issue 8, p1442. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Gaussian+function%22">Gaussian function</searchLink><br /><searchLink fieldCode="DE" term="%22Infrared+imaging%22">Infrared imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Grayscale+model%22">Grayscale model</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22False+alarms%22">False alarms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17081442 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1442 Subjects: – SubjectFull: Gaussian function Type: general – SubjectFull: Infrared imaging Type: general – SubjectFull: Grayscale model Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: False alarms Type: general Titles: – TitleFull: Infrared Small Target Detection Based on Entropy Variation Weighted Local Contrast Measure. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xi, Yuyang – PersonEntity: Name: NameFull: Zhang, Yushan – PersonEntity: Name: NameFull: Jiang, Ying – PersonEntity: Name: NameFull: Zhang, Liuwei – PersonEntity: Name: NameFull: Hou, Qingyu IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 04 Text: Apr2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 8 Titles: – TitleFull: Remote Sensing Type: main |
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