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快速高分辨率图像选择的方法:FAHRIS算法。

Methodological approach for fast high-resolution image selection: FAHRIS algorithm.

作者信息

Åsebø Bjørnar, Cavazzani Stefano, Bertolin Chiara, Bettanini Carlo, Fusato Giampaolo, Bertolo Andrea, Fiorentin Pietro

机构信息

Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway.

Department of Physics and Astronomy, University of Padua, Padua, Italy.

出版信息

MethodsX. 2024 Nov 28;13:103072. doi: 10.1016/j.mex.2024.103072. eCollection 2024 Dec.

DOI:10.1016/j.mex.2024.103072
PMID:39717119
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11664006/
Abstract

Recent research highlights advancements in collecting Artificial Light at Night (ALAN) data using radiosondes on stratospheric balloons, revealing a need for enhanced in-flight image stabilization. This paper proposes a twofold approach: Firstly, it introduces a design concept for a high-resolution image acquisition and stabilization system for aerial instruments (e.g., drones, balloons). Secondly, it presents a novel Fast Algorithm for High-Resolution Image Selection (FAHRIS) for rapid image selection, grouping and stitching of acquired imagery. FAHRIS' effectiveness is validated using datasets from three flights over Italy: a stratospheric balloon flight reaching 34 kms over Florence, and drone flights using a DJI Mavic 2 up to 253 m over Trevisoand 330 m over Padua. Limitations and challenges encountered during the validation of FAHRIS, such as computational constraints affecting dataset processing, are addressed. Additionally, the results of the image stitching process highlight potential distortions and stretching issues, particularly evident in images with significant relative angles.•Design proposition of stabilization system for aerial instruments.•Development of a novel and fast image selection, grouping and stitching algorithm (FAHRIS).•Validation of algorithm against data sets from three flights.

摘要

最近的研究突出了利用平流层气球上的无线电探空仪收集夜间人造光(ALAN)数据方面的进展,这表明需要增强飞行中的图像稳定性。本文提出了一种双重方法:首先,介绍了一种用于航空仪器(如无人机、气球)的高分辨率图像采集和稳定系统的设计概念。其次,提出了一种新颖的高分辨率图像选择快速算法(FAHRIS),用于对采集的图像进行快速图像选择、分组和拼接。使用来自意大利三次飞行的数据集验证了FAHRIS的有效性:一次平流层气球飞行在佛罗伦萨上空达到34公里,以及使用大疆Mavic 2无人机在特雷维索上空253米和帕多瓦上空330米的飞行。解决了在FAHRIS验证过程中遇到的限制和挑战,如影响数据集处理的计算限制。此外,图像拼接过程的结果突出了潜在的扭曲和拉伸问题,在具有显著相对角度的图像中尤为明显。•航空仪器稳定系统的设计方案。•开发一种新颖且快速的图像选择、分组和拼接算法(FAHRIS)。•根据三次飞行的数据集对算法进行验证。

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2
Verification of Angular Response of Sky Quality Meter with Quasi-Punctual Light Sources.使用准点光源验证天空质量测量仪的角度响应
Sensors (Basel). 2021 Nov 13;21(22):7544. doi: 10.3390/s21227544.