Linear feature detection algorithm for astronomical surveys - I. Algorithm description

TL;DR

This paper introduces a novel linear feature detection algorithm tailored for astronomical surveys, enhancing detection of linear trails like meteors while reducing false positives and improving efficiency in Big Data astronomy applications.

Contribution

The paper presents a new linear feature detection algorithm that combines object removal, line enhancement, and false positive reduction, specifically designed for large-scale astronomical data.

Findings

Effective detection of linear features in astronomical images.

Significant reduction in false positives.

Suitable for implementation in Big Data astronomy surveys.

Abstract

Computer vision algorithms are powerful tools in astronomical image analyses, especially when automation of object detection and extraction is required. Modern object detection algorithms in astronomy are oriented towards detection of stars and galaxies, ignoring completely detection of existing linear features. With the emergence of wide-field sky surveys, linear features attract scientific interest as possible trails of fast flybys of near-Earth asteroids and meteors. In this work we describe a new linear feature detection algorithm designed specifically for implementation in Big Data astronomy. The algorithm combines a series of algorithmic steps that first remove other objects (stars, galaxies) from the image and then enhance the line to enable more efficient line detection with the Hough algorithm. The rate of false positives is greatly reduced thanks to a step that replaces possible line segments with rectangles and then compares lines fitted to the rectangles with the lines obtained directly from the image. The speed of the algorithm and its applicability in astronomical surveys are also discussed.