Optimal and Efficient Streak Detection in Astronomical Images
Abstract
Identification of linear features (streaks) in astronomical images is important for several reasons, including: detecting fast-moving near-Earth asteroids; detecting or flagging faint satellites streaks; and flagging or removing diffraction spikes, pixel bleeding, line-like cosmic rays and bad-pixel features. Here we discuss an efficient and optimal algorithm for the detection of such streaks. The optimal method to detect streaks in astronomical images is by cross-correlating the image with a template of a line broadened by the point-spread function of the system. To do so efficiently, the cross-correlation of the streak position and angle is performed using the Radon transform, which is the integral of pixel values along all possible lines through an image. A fast version of the Radon transform exists, which we here extend to efficiently detect arbitrarily short lines. While the brute force Radon transform requires { \mathcal O }({N}3) operations for a N × N image, the fast Radon transform has a complexity of { \mathcal O }({N}2{log}(N)). We apply this method to simulated images, recovering the theoretical signal-to-noise ratio, and to real images, finding long streaks of low-Earth-orbit satellites and shorter streaks of Global Positioning System satellites. We detect streaks that are barely visible to the eye, out of hundreds of images, without a-priori knowledge of the streaks’ positions or angles. We provide implementation of this algorithm in Python and MATLAB.
- Publication:
-
The Astronomical Journal
- Pub Date:
- November 2018
- DOI:
- 10.3847/1538-3881/aaddff
- arXiv:
- arXiv:1806.04204
- Bibcode:
- 2018AJ....156..229N
- Keywords:
-
- methods: data analysis;
- techniques: image processing;
- Astrophysics - Instrumentation and Methods for Astrophysics
- E-Print:
- doi:10.3847/1538-3881/aaddff