Infrared Small Target Detection Based on Gradient-Intensity Joint Saliency Measure

Abstract

Small target detection is an arduous mission in infrared search and tracking (IRST) system, especially when the target signal is disturbed by high-intensity background clutters. In view of this situation, this paper presents a robust target detection algorithm based on gradient-intensity joint saliency measure (GISM) to gradually eliminate complex background clutter. Because of thermal remote sensing imaging, the infrared target usually occupies a small area that accords with the optics point spread function (PSF), so it can be distinguished from the background clutter in both gradient and intensity properties. According to this, firstly, the original image is transformed into a gradient map, and the gradient saliency measure (GSM) is calculated to highlight the target signal and suppress the sharp edge clutter, so the candidate targets can be reliably extracted by using the maximum entropy principle. Secondly, the local intensity saliency measure (LISM) is obtained by calculating the gray difference between each candidate region and its local surroundings, so as to preserve the real target and remove intense structural clutter such as black holes or corners. Finally, by fully integrating the gradient and intensity properties, the GISM defined by LISM weighted GSM map can efficiently identify the real target signal and eliminate false alarms. Experimental results prove that the proposed method not only has advantages in background clutter suppression and small target enhancement, but also has reasonable time consumption.