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卫星图像在鲸鱼调查中的潜力。

The Potential of Satellite Imagery for Surveying Whales.

机构信息

BioConsult SH GmbH & Co.KG, Schobüller Str. 36, 25813 Husum, Germany.

British Antarctic Survey, High Cross, Madingley Road, Cambridge CB3 0ET, UK.

出版信息

Sensors (Basel). 2021 Feb 1;21(3):963. doi: 10.3390/s21030963.

DOI:10.3390/s21030963
PMID:33535463
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7867100/
Abstract

The emergence of very high-resolution (VHR) satellite imagery (less than 1 m spatial resolution) is creating new opportunities within the fields of ecology and conservation biology. The advancement of sub-meter resolution imagery has provided greater confidence in the detection and identification of features on the ground, broadening the realm of possible research questions. To date, VHR imagery studies have largely focused on terrestrial environments; however, there has been incremental progress in the last two decades for using this technology to detect cetaceans. With advances in computational power and sensor resolution, the feasibility of broad-scale VHR ocean surveys using VHR satellite imagery with automated detection and classification processes has increased. Initial attempts at automated surveys are showing promising results, but further development is necessary to ensure reliability. Here we discuss the future directions in which VHR satellite imagery might be used to address urgent questions in whale conservation. We highlight the current challenges to automated detection and to extending the use of this technology to all oceans and various whale species. To achieve basin-scale marine surveys, currently not feasible with any traditional surveying methods (including boat-based and aerial surveys), future research requires a collaborative effort between biology, computation science, and engineering to overcome the present challenges to this platform's use.

摘要

高分辨率(VHR)卫星图像(空间分辨率小于 1 米)的出现为生态学和保护生物学领域创造了新的机会。亚米级分辨率图像的进步提高了对地面特征的检测和识别的信心,拓宽了可能的研究问题的范围。迄今为止,VHR 图像研究主要集中在陆地环境上;然而,在过去二十年中,利用这项技术来检测鲸鱼已经取得了渐进的进展。随着计算能力和传感器分辨率的提高,使用 VHR 卫星图像进行自动检测和分类的大规模 VHR 海洋调查的可行性已经增加。自动调查的初步尝试显示出有希望的结果,但需要进一步的发展来确保可靠性。在这里,我们讨论了 VHR 卫星图像可能用于解决鲸鱼保护中紧迫问题的未来方向。我们强调了当前在自动检测方面以及在将这项技术扩展到所有海洋和各种鲸鱼物种方面面临的挑战。为了实现目前任何传统调查方法(包括船载和航空调查)都不可行的流域尺度海洋调查,未来的研究需要生物学、计算科学和工程学之间的合作努力,以克服该平台使用的现有挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36de/7867100/cc53ee95691c/sensors-21-00963-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36de/7867100/cc53ee95691c/sensors-21-00963-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36de/7867100/cc53ee95691c/sensors-21-00963-g001.jpg

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Whale counting in satellite and aerial images with deep learning.使用深度学习技术对卫星和航空图像中的鲸鱼进行计数。
MethodsX. 2023 Jan 25;10:102040. doi: 10.1016/j.mex.2023.102040. eCollection 2023.
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PeerJ. 2022 Jun 20;10:e13540. doi: 10.7717/peerj.13540. eCollection 2022.
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