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标注超高分辨率卫星图像：一个鲸鱼案例研究。

Annotating very high-resolution satellite imagery: A whale case study.

作者信息

Cubaynes Hannah Charlotte, Clarke Penny Joanna, Goetz Kimberly Thea, Aldrich Tyler, Fretwell Peter Thomas, Leonard Kathleen Elise, Khan Christin Brangwynne

机构信息

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

School of Engineering, The University of Edinburgh, Sanderson Building, Robert Stevenson Road, The King's Buildings, Edinburgh, EH9 3FB, United Kingdom.

出版信息

MethodsX. 2023 Jan 25;10:102040. doi: 10.1016/j.mex.2023.102040. eCollection 2023.

DOI:10.1016/j.mex.2023.102040
PMID:36793672
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9923222/
Abstract

The use of very high-resolution (VHR) optical satellites is gaining momentum in the field of wildlife monitoring, particularly for whales, as this technology is showing potential for monitoring the less studied regions. However, surveying large areas using VHR optical satellite imagery requires the development of automated systems to detect targets. Machine learning approaches require large training datasets of annotated images. Here we propose a standardised workflow to annotate VHR optical satellite imagery using ESRI ArcMap 10.8, and ESRI ArcGIS Pro 2.5., using cetaceans as a case study, to develop AI-ready annotations.•A step-by-step protocol to review VHR optical satellite images and annotate the features of interest.•A step-by-step protocol to create bounding boxes encompassing the features of interest.•A step-by-step guide to clip the satellite image using bounding boxes to create image chips.

摘要

在野生动物监测领域，尤其是对鲸鱼的监测中，超高分辨率（VHR）光学卫星的应用正日益兴起，因为这项技术在监测研究较少的区域方面显示出了潜力。然而，使用VHR光学卫星图像进行大面积测量需要开发自动目标检测系统。机器学习方法需要大量带注释图像的训练数据集。在此，我们提出一种标准化工作流程，以鲸类为案例研究，使用ESRI ArcMap 10.8和ESRI ArcGIS Pro 2.5对VHR光学卫星图像进行注释，以开发适用于人工智能的注释。

• 逐步审查VHR光学卫星图像并注释感兴趣特征的协议。

• 逐步创建包含感兴趣特征的边界框的协议。

• 使用边界框裁剪卫星图像以创建图像芯片的逐步指南。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b14d/9923222/cf04aca69aaa/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b14d/9923222/778e1f7b6af8/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b14d/9923222/dbdb9fed2fa8/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b14d/9923222/718afb5d74eb/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b14d/9923222/cf04aca69aaa/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b14d/9923222/778e1f7b6af8/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b14d/9923222/dbdb9fed2fa8/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b14d/9923222/718afb5d74eb/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b14d/9923222/cf04aca69aaa/gr4.jpg

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Annotating very high-resolution satellite imagery: A whale case study.标注超高分辨率卫星图像：一个鲸鱼案例研究。
MethodsX. 2023 Jan 25;10:102040. doi: 10.1016/j.mex.2023.102040. eCollection 2023.
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本文引用的文献

1
Remote sensing techniques for automated marine mammals detection: a review of methods and current challenges.遥感技术在自动海洋哺乳动物检测中的应用：方法综述及当前挑战。
PeerJ. 2022 Jun 20;10:e13540. doi: 10.7717/peerj.13540. eCollection 2022.
2
Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models.鲸鱼太空数据集，一个用于训练机器学习模型的鲸鱼标注卫星图像数据集。
Sci Data. 2022 May 27;9(1):245. doi: 10.1038/s41597-022-01377-4.
3
Mapping Arctic cetaceans from space: A case study for beluga and narwhal.
从太空绘制北极鲸类地图：以白鲸和独角鲸为例。
PLoS One. 2021 Aug 4;16(8):e0254380. doi: 10.1371/journal.pone.0254380. eCollection 2021.
4
The Potential of Satellite Imagery for Surveying Whales.卫星图像在鲸鱼调查中的潜力。
Sensors (Basel). 2021 Feb 1;21(3):963. doi: 10.3390/s21030963.
5
A comparison of baleen whale density estimates derived from overlapping satellite imagery and a shipborne survey.基于重叠卫星图像和船舶调查的须鲸密度估计值比较。
Sci Rep. 2020 Jul 31;10(1):12985. doi: 10.1038/s41598-020-69887-y.
6
Using remote sensing to detect whale strandings in remote areas: The case of sei whales mass mortality in Chilean Patagonia.利用遥感技术探测偏远地区的鲸鱼搁浅事件：智利巴塔哥尼亚地区塞鲸大规模死亡事件。
PLoS One. 2019 Oct 17;14(10):e0222498. doi: 10.1371/journal.pone.0222498. eCollection 2019.
7
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。
Sci Rep. 2019 Oct 3;9(1):14259. doi: 10.1038/s41598-019-50795-9.
8
Aerial-trained deep learning networks for surveying cetaceans from satellite imagery.基于航空训练的深度学习网络，可从卫星图像中调查鲸目动物。
PLoS One. 2019 Oct 1;14(10):e0212532. doi: 10.1371/journal.pone.0212532. eCollection 2019.
9
Largest baleen whale mass mortality during strong El Niño event is likely related to harmful toxic algal bloom.在强烈厄尔尼诺事件期间须鲸的最大规模死亡可能与有害有毒藻华有关。
PeerJ. 2017 May 31;5:e3123. doi: 10.7717/peerj.3123. eCollection 2017.
10
State-space mark-recapture estimates reveal a recent decline in abundance of North Atlantic right whales.状态空间标记重捕估计显示北大西洋露脊鲸的数量最近有所下降。
Ecol Evol. 2017 Sep 18;7(21):8730-8741. doi: 10.1002/ece3.3406. eCollection 2017 Nov.