Abstract
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.
Highlights
Infrared small target detection has been a hot topic for guidance, defense, navigation, infrared search and track and other photoelectric imaging systems [1,2,3,4]
In order to further illustrate the superiority of our method, this paper introduces two evaluation indicators: signal-to-clutter ratiothe gain (SCRG) and background suppression factor (BSF)
We present an efficient and robust infrared small target detection method based
Summary
Infrared small target detection has been a hot topic for guidance, defense, navigation, infrared search and track and other photoelectric imaging systems [1,2,3,4]. Zhao et al [17] proposed a new detection method based on nonsubsampled contourlet transform (NSCT) to achieve high detection rate in low SNR infrared image. WBCT is utilized to decompose the original infrared image into multiscale and multidirectional sub-bands, and an adaptive threshold selection strategy is applied to select the background information in high frequency sub-bands, and the coarse target image is obtained by making a difference between the original image and the background image after the Inverse Wavelet-based. An effective kurtosis map calculated by local kurtosis operator combined with the adaptive threshold segmentation mechanism is applied to process the coarse target image, by which the residual background edge is efficiently eliminated. Extensive experimental results demonstrate that the proposed method can achieve satisfactory performance
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