Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background
Wavelet-based Contourlet transform (WBCT) is a typical Multi-scale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/3/755 |
| _version_ | 1811186295428349952 |
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| author | He Wang Yunhong Xin |
| author_facet | He Wang Yunhong Xin |
| author_sort | He Wang |
| collection | DOAJ |
| description | Wavelet-based Contourlet transform (WBCT) is a typical Multi-scale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In this paper, we present an efficient and robust method which utilizes WBCT method in conjunction with kurtosis model for the infrared small target detection in complex background. We mainly made two contributions. The first, WBCT method is introduced as a preprocessing step, and meanwhile we present an adaptive threshold selection strategy for the selection of WBCT coefficients of different scales and different directions, as a result, the most background clutters are suppressed in this stage. The second, a kurtosis saliency map is obtained by using a local kurtosis operator. In the kurtosis saliency map, a slide window and its corresponding mean and variance is defined to locate the area where target exists, and subsequently an adaptive threshold segment mechanism is utilized to pick out the small target from the selected area. Extensive experimental results demonstrate that, compared with the contrast methods, the proposed method can achieve satisfactory performance, and it is superior in detection rate, false alarm rate and ROC curve especially in complex background. |
| first_indexed | 2024-04-11T13:43:10Z |
| format | Article |
| id | doaj.art-9226909f783741209521a1c768803e36 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:43:10Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9226909f783741209521a1c768803e362022-12-22T04:21:10ZengMDPI AGSensors1424-82202020-01-0120375510.3390/s20030755s20030755Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex BackgroundHe Wang0Yunhong Xin1School of Physics and Information Technology, Shaanxi Normal University, Xi’an 710119, ChinaSchool of Physics and Information Technology, Shaanxi Normal University, Xi’an 710119, ChinaWavelet-based Contourlet transform (WBCT) is a typical Multi-scale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In this paper, we present an efficient and robust method which utilizes WBCT method in conjunction with kurtosis model for the infrared small target detection in complex background. We mainly made two contributions. The first, WBCT method is introduced as a preprocessing step, and meanwhile we present an adaptive threshold selection strategy for the selection of WBCT coefficients of different scales and different directions, as a result, the most background clutters are suppressed in this stage. The second, a kurtosis saliency map is obtained by using a local kurtosis operator. In the kurtosis saliency map, a slide window and its corresponding mean and variance is defined to locate the area where target exists, and subsequently an adaptive threshold segment mechanism is utilized to pick out the small target from the selected area. Extensive experimental results demonstrate that, compared with the contrast methods, the proposed method can achieve satisfactory performance, and it is superior in detection rate, false alarm rate and ROC curve especially in complex background.https://www.mdpi.com/1424-8220/20/3/755infrared small target detectionwavelet-based contourlet transform (wbct)kurtosis mapcomplex background |
| spellingShingle | He Wang Yunhong Xin Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background Sensors infrared small target detection wavelet-based contourlet transform (wbct) kurtosis map complex background |
| title | Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background |
| title_full | Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background |
| title_fullStr | Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background |
| title_full_unstemmed | Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background |
| title_short | Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background |
| title_sort | wavelet based contourlet transform and kurtosis map for infrared small target detection in complex background |
| topic | infrared small target detection wavelet-based contourlet transform (wbct) kurtosis map complex background |
| url | https://www.mdpi.com/1424-8220/20/3/755 |
| work_keys_str_mv | AT hewang waveletbasedcontourlettransformandkurtosismapforinfraredsmalltargetdetectionincomplexbackground AT yunhongxin waveletbasedcontourlettransformandkurtosismapforinfraredsmalltargetdetectionincomplexbackground |