Precision Detection of Infrared Small Target in Ground-to-Air Scene - Xiaona Dong; Huilin Jiang; Yansong Song; Keyan Dong (Remote Sensing)
Reliable infrared small target detection plays an important role in infrared search and track systems. In recent years, most target detection methods usually use the statistical features of a rectangular window to represent the contrast between the target and the background. When the size of the target is small or the target is close to the background, the statistical features of the rectangular window would reduce the significance of the target. Moreover, such methods have limited effect on interfering targets, high brightness background, background edges, and clutter suppression in complex backgrounds, and are likely to misdetect the target or even miss it. This paper proposes a non-window, structured algorithm for precision detection of infrared small targets under ground-to-air complex scenes. The non-window, structured local grayscale descent intensity and local gradient watershed (LGDI-LGW) filter can detect a 1 × 1 pixel infrared small target, and effectively suppress interfering targets and background edges. By using the adaptive threshold and centroid algorithm on the target area, the precision of target coordinates reaches sub-pixel accuracy. The results of 9 simulation experiments show that the algorithm has the lowest false alarm rate and the highest detection rate compared with the eight baseline algorithms. It can effectively detect targets with Gaussian distribution of grayscale values and targets with grayscale values approximating tree stump structure. The results of 2 engineering experiments show that under simulated near-sun conditions, a uniform target is precisely detected, and the UAV point target is precisely detected in complex ground-to-air scenes.