Implementation of DBSCAN Method in Star Trackers to Improve Image Segmentation in Heavy Noise Conditions

Abstract

Star trackers are currently the most accurate sensors for determining the attitude of a spacecraft. These sensors comprise not only highly capable optical detectors and processor units but also complicated software solvers. One of the main solvers employed in star trackers is image segmentation. In this study, the aim is to develop a hybrid image segmentation method which is a combination of both global thresholding and density-based spatial clustering of applications with noise (DBSCAN) method to increase detection probability of the stars in heavy noise. Secondly, a sorting algorithm is added to list the detected stars in terms of their brightness to increase the efficiency of the star tracking algorithm. Then, this new approach and two different conventional segmentation methods are applied to the Orion star constellation image polluted with Gaussian, salt and pepper, and uneven background noises. The resulting images of these segmentation methods are compared in terms of denoising capabilities. Although computationally more expensive, the proposed DBSCAN-based hybrid method displays a background pixel recovery performance of 99.5%, compared to Otsu global thresholding and adaptive thresholding methods’ 73.5% and 79.9% recovery values, respectively. Additionally, it has been demonstrated that the sorting algorithm successfully listed the detected stars in accordance with their brightness.