Space Imaging Point Source Detection and Characterization

• Type: Another Publication in an International Scientific Journal
• Date: 2024

Authors

Ribeiro, FSF (Author) Other; P. J. V. Garcia (Author) FEUP; Silva, M (Author) Other; Jaime S Cardoso (Author) FEUP

Journal

Title: IEEE Access

Vol. 12

ISSN: 2169-3536

Publisher: IEEE

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-011-09E

DOI: 10.1109/access.2024.3420217

Abstract (EN): Point source detection algorithms play a pivotal role across diverse applications, influencing fields such as astronomy, biomedical imaging, environmental monitoring, and beyond. This article reviews the algorithms used for space imaging applications from ground and space telescopes. The main difficulties in detection arise from the incomplete knowledge of the impulse function of the imaging system, which depends on the aperture, atmospheric turbulence (for ground-based telescopes), and other factors, some of which are time-dependent. Incomplete knowledge of the impulse function decreases the effectiveness of the algorithms. In recent years, deep learning techniques have been employed to mitigate this problem and have the potential to outperform more traditional approaches. The success of deep learning techniques in object detection has been observed in many fields, and recent developments can further improve the accuracy. However, deep learning methods are still in the early stages of adoption and are used less frequently than traditional approaches. In this review, we discuss the main challenges of point source detection, as well as the latest developments, covering both traditional and current deep learning methods. In addition, we present a comparison between the two approaches to better demonstrate the advantages of each methodology.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19