# A Line Segment Detector for Space Target Images Robust to Complex Illumination
## 1 Introduction
HT is one of the most commonly used line detection algorithms [19]. It cleverly converts the line segment detection problem in the image domain into a peak detection problem in the parameter space. HT has good robustness to edge gaps and image noise, but it also has some shortcomings, such as complex threshold settings, a large number of false alarms, and a large amount of computation. For the shortcomings of the HT, many improved Hough detectors have been proposed. The improved Hough detectors mainly focus on improving the performance and reducing the complexity of the algorithm. [PERSON] et al. [20] proposed a probabilistic Hough transform (PHT) method, which uses only a small number of randomly selected edge points in the image as the input to the Hough transform. [PERSON] et al. [21] improved the PHT by proposing a progressive probabilistic Hough transform (PPHT) method, which accelerates the calculation of HT by the selection of random edge points. [PERSON] et al. [22] proposed a random Hough transform (RHT) method. For a curve with n parameters, it improves efficiency by randomly selecting n pixels and mapping them to a point in the parameter space. [PERSON] et al. [23] proposed a kernel-based Hough transform (KHT) method, which improves the robustness of HT using an effective voting scheme. [PERSON] et al. [24] proposed a method that could accurately extract the endpoints of line segments, which uses the geometric analysis method to extract inherent features of line segments from the voting distribution near the HT peak. [PERSON] et al. [25] proposed a Markov chain marginal line segment detector (MCMLSD) based on PPHT, which combines the advantages of PPHT and image domain spatial analysis to identify line segments, but it produces many false positives in complex real images. The perceptual grouping (PG) method solves the line segment detection problem from a completely different perspective. This type of method first uses the gradient magnitude and direction to group pixels, then fits the pixels in the same group into line segments, and finally suppresses the false alarm of line segments by verification methods. [PERSON] et al. [26] proposed the first line segment detector based on gradient direction, but this detector requires manual parameter adjustment and has false detections of line segments. To control false positives, [PERSON] et al. [27] proposed the Helmholtz principle to verify line segments, which converts the detection threshold to the number of false alarms (NFA) by employing the inverse uniform random assumption. [PERSON] [28] et al. extended the application of the Helmholtz principle by using the relative number of false alarms (RNFA) instead of the NFA. In a subsequent study, [PERSON] et al. [29] introduced the Helmholtz principle and proposed an advanced line segment detector (LSD), which effectively solves the problems of manual parameter adjustment and false alarm suppression. [PERSON] et al. [30; 31; 32] proposed a faster line segment detector (EDlines), which provided the possibility of real-time application of line segment detection. Similarly, [PERSON] et al. [33] proposed an adaptive gradient threshold and full-direction line segment growth method based on line detection. To solve the problem of manual parameter adjustment, [PERSON] et al. [34] improved the Canny detector and proposed a parameter-free detector (Cannylines), which has better robustness in manmade scenes. Aiming at the problem of the line segment oversegmentation, [PERSON] et al. [35] proposed a multi-scale extension method for LSD, this method effectively solves the over-segmentation problem and is more robust to low-contrast noise. [PERSON] et al. [36] also proposed a similar improved method. [PERSON] et al. [37] proposed a method to merge fracture line segments. [PERSON] et al. [38] proposed a new line segment detection method (Linelet), which can extract more effective line segments, but it cannot overcome the problem of line segment breaks under the condition of gradient instability.