Infrared small target detection algorithm based on spatial dissimilarity weighted local contrast

Abstract

To solve the problem that the local contrast algorithm is easily influenced by the heavy clutter background or strong noise and does not fully consider the spatial neighbourhood characteristics of the small target, a spatial dissimilarity weighted local contrast-based method (SDWLCM) for infrared small target detection is proposed in this paper. Firstly, the two-dimensional difference of the Gaussian filter with central excitation and lateral suppression characteristics is chosen to preprocess the original infrared image for removing the flat background and improving the signal-to-noise ratio (SNR) of the small target. Secondly, the spatial dissimilarity between the target and its surrounding backgrounds is designed for local contrast weighting to generate the contrast saliency map so that the heavy clutter background is greatly suppressed and the small target is further highlighted. Thirdly, the saliency map is segmented by the adaptive threshold to get the real targets. Experimental results show that, compared with other methods, the SDWLCM, which owns not only a higher SNR gain and a larger background suppression factor but also a higher detection rate and a lower false alarm rate, is confirmed to be an effective method to detect the small targets.