Infrared Small Target Detection Using Directional Derivative Correlation Filtering and a Relative Intensity Contrast Measure
Detecting small targets in infrared search and track (IRST) systems in complex backgrounds poses a significant challenge. This study introduces a novel detection framework that integrates directional derivative correlation filtering (DDCF) with a local relative intensity contrast measure (LRICM) to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/11/1921 |
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| _version_ | 1849330797574619136 |
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| author | Feng Xie Dongsheng Yang Yao Yang Tao Wang Kai Zhang |
| author_facet | Feng Xie Dongsheng Yang Yao Yang Tao Wang Kai Zhang |
| author_sort | Feng Xie |
| collection | DOAJ |
| description | Detecting small targets in infrared search and track (IRST) systems in complex backgrounds poses a significant challenge. This study introduces a novel detection framework that integrates directional derivative correlation filtering (DDCF) with a local relative intensity contrast measure (LRICM) to effectively handle diverse background disturbances, including cloud edges and structural corners. This approach involves converting the original infrared image into an infrared gradient vector field (IGVF) using a facet model. Exploiting the distinctive characteristics of small targets in second-order derivative computations, four directional filters are designed to emphasize target features while suppressing edge clutter. The DDCF map is then constructed by merging the results of the second-order derivative filters applied in four distinct orientations. Subsequently, the LRICM is determined by analyzing the gray-level contrast between the target and its immediate surroundings, effectively minimizing interference from background elements like corners. The final detection step involves fusing the DDCF and LRICM maps to generate a comprehensive saliency representation, which is then processed using an adaptive thresholding technique to extract small targets accurately. Experimental evaluations across multiple datasets verify that the proposed method substantially improves the signal-to-clutter ratio (SCR). Compared to existing advanced techniques, the proposed approach demonstrates superior detection reliability in challenging environments, including ground surfaces, cloudy conditions, forested areas, and urban structures. Moreover, the framework maintains low computational complexity, achieving a favorable balance between detection accuracy and efficiency, thereby demonstrating promising potential for deployment in practical IRST scenarios. |
| format | Article |
| id | doaj-art-1bf5cd4809f1416987bd071c279ebf99 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-1bf5cd4809f1416987bd071c279ebf992025-08-20T03:46:49ZengMDPI AGRemote Sensing2072-42922025-05-011711192110.3390/rs17111921Infrared Small Target Detection Using Directional Derivative Correlation Filtering and a Relative Intensity Contrast MeasureFeng Xie0Dongsheng Yang1Yao Yang2Tao Wang3Kai Zhang4School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 517108, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 517108, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, ChinaDetecting small targets in infrared search and track (IRST) systems in complex backgrounds poses a significant challenge. This study introduces a novel detection framework that integrates directional derivative correlation filtering (DDCF) with a local relative intensity contrast measure (LRICM) to effectively handle diverse background disturbances, including cloud edges and structural corners. This approach involves converting the original infrared image into an infrared gradient vector field (IGVF) using a facet model. Exploiting the distinctive characteristics of small targets in second-order derivative computations, four directional filters are designed to emphasize target features while suppressing edge clutter. The DDCF map is then constructed by merging the results of the second-order derivative filters applied in four distinct orientations. Subsequently, the LRICM is determined by analyzing the gray-level contrast between the target and its immediate surroundings, effectively minimizing interference from background elements like corners. The final detection step involves fusing the DDCF and LRICM maps to generate a comprehensive saliency representation, which is then processed using an adaptive thresholding technique to extract small targets accurately. Experimental evaluations across multiple datasets verify that the proposed method substantially improves the signal-to-clutter ratio (SCR). Compared to existing advanced techniques, the proposed approach demonstrates superior detection reliability in challenging environments, including ground surfaces, cloudy conditions, forested areas, and urban structures. Moreover, the framework maintains low computational complexity, achieving a favorable balance between detection accuracy and efficiency, thereby demonstrating promising potential for deployment in practical IRST scenarios.https://www.mdpi.com/2072-4292/17/11/1921directional derivative correlation filtering (DDCF)local relative intensity contrast measure (LRICM)infrared imagingsmall target detection |
| spellingShingle | Feng Xie Dongsheng Yang Yao Yang Tao Wang Kai Zhang Infrared Small Target Detection Using Directional Derivative Correlation Filtering and a Relative Intensity Contrast Measure Remote Sensing directional derivative correlation filtering (DDCF) local relative intensity contrast measure (LRICM) infrared imaging small target detection |
| title | Infrared Small Target Detection Using Directional Derivative Correlation Filtering and a Relative Intensity Contrast Measure |
| title_full | Infrared Small Target Detection Using Directional Derivative Correlation Filtering and a Relative Intensity Contrast Measure |
| title_fullStr | Infrared Small Target Detection Using Directional Derivative Correlation Filtering and a Relative Intensity Contrast Measure |
| title_full_unstemmed | Infrared Small Target Detection Using Directional Derivative Correlation Filtering and a Relative Intensity Contrast Measure |
| title_short | Infrared Small Target Detection Using Directional Derivative Correlation Filtering and a Relative Intensity Contrast Measure |
| title_sort | infrared small target detection using directional derivative correlation filtering and a relative intensity contrast measure |
| topic | directional derivative correlation filtering (DDCF) local relative intensity contrast measure (LRICM) infrared imaging small target detection |
| url | https://www.mdpi.com/2072-4292/17/11/1921 |
| work_keys_str_mv | AT fengxie infraredsmalltargetdetectionusingdirectionalderivativecorrelationfilteringandarelativeintensitycontrastmeasure AT dongshengyang infraredsmalltargetdetectionusingdirectionalderivativecorrelationfilteringandarelativeintensitycontrastmeasure AT yaoyang infraredsmalltargetdetectionusingdirectionalderivativecorrelationfilteringandarelativeintensitycontrastmeasure AT taowang infraredsmalltargetdetectionusingdirectionalderivativecorrelationfilteringandarelativeintensitycontrastmeasure AT kaizhang infraredsmalltargetdetectionusingdirectionalderivativecorrelationfilteringandarelativeintensitycontrastmeasure |