气候变化领域集成服务门户(CCPortal): SealNet: A fully-automated pack-ice seal detection pipeline for sub-meter satellite imagery

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DOI	10.1016/j.rse.2019.111617
	SealNet: A fully-automated pack-ice seal detection pipeline for sub-meter satellite imagery
	Gonçalves B.C.; Spitzbart B.; Lynch H.J.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	Antarctic pack-ice seals, a group of four species of true seals (Phocidae), play a pivotal role in the Southern Ocean foodweb as wide-ranging predators of Antarctic krill (Euphausia superba). Due to their circumpolar distribution and the remoteness and vastness of their habitat, little is known about their population sizes. Estimating pack-ice seal population sizes and trends is key to understanding how the Southern Ocean ecosystem will react to threats such as climate change driven sea ice loss and krill fishing. We present a functional pack-ice seal detection pipeline using Worldview-3 imagery and a Convolutional Neural Network that counts and locates seal centroids. We propose a new CNN architecture that detects objects by combining semantic segmentation heatmaps with binary classification and counting by regression. Our pipeline locates over 30% of seals, when compared to consensus counts from human experts, and reduces the time required for seal detection by 95% (assuming just a single GPU). While larger training sets and continued algorithm development will no doubt improve classification accuracy, our pipeline, which can be easily adapted for other large-bodied animals visible in sub-meter satellite imagery, demonstrates the potential for machine learning to vastly expand our capacity for regular pack-ice seal surveys and, in doing so, will contribute to ongoing international efforts to monitor pack-ice seals. © 2019
英文关键词	APIS; CCAMLR; Crabeater seal; Deep learning; Leptonychotes weddellii; Lobodon carcinophaga; Object detection; Segmentation; Very high resolution; Weddell seal
语种	英语
scopus关键词	Classification (of information); Climate change; Deep learning; Image enhancement; Image segmentation; Machine learning; Mammals; Neural networks; Object detection; Population statistics; Satellite imagery; Sea ice; Semantics; APIS; CCAMLR; Leptonychotes weddellii; Lobodon carcinophaga; Very high resolution; Weddell seals; Pipelines; algorithm; climate change; image resolution; learning; pinniped; pipeline; satellite imagery; sea ice; segmentation; snowpack; WorldView; Southern Ocean; Weddell Sea; Animalia; Euphausia superba; Euphausiacea; Leptonychotes weddellii; Lobodon carcinophaga; Lobodon carcinophagus; Phocidae
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179465
作者单位	610 Life Sciences Building, Department of Ecology and Evolution, Stony Brook, NY 11777, United States
推荐引用方式 GB/T 7714	Gonçalves B.C.,Spitzbart B.,Lynch H.J.. SealNet: A fully-automated pack-ice seal detection pipeline for sub-meter satellite imagery[J],2020,239.
APA	Gonçalves B.C.,Spitzbart B.,&Lynch H.J..(2020).SealNet: A fully-automated pack-ice seal detection pipeline for sub-meter satellite imagery.Remote Sensing of Environment,239.
MLA	Gonçalves B.C.,et al."SealNet: A fully-automated pack-ice seal detection pipeline for sub-meter satellite imagery".Remote Sensing of Environment 239(2020).