School of Natural Resources and Environment, University of Florida, Gainesville, FL, United States of America.
School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, United States of America.
PeerJ. 2022 Jun 20;10:e13540. doi: 10.7717/peerj.13540. eCollection 2022.
Marine mammals are under pressure from multiple threats, such as global climate change, bycatch, and vessel collisions. In this context, more frequent and spatially extensive surveys for abundance and distribution studies are necessary to inform conservation efforts. Marine mammal surveys have been performed visually from land, ships, and aircraft. These methods can be costly, logistically challenging in remote locations, dangerous to researchers, and disturbing to the animals. The growing use of imagery from satellite and unoccupied aerial systems (UAS) can help address some of these challenges, complementing crewed surveys and allowing for more frequent and evenly distributed surveys, especially for remote locations. However, manual counts in satellite and UAS imagery remain time and labor intensive, but the automation of image analyses offers promising solutions. Here, we reviewed the literature for automated methods applied to detect marine mammals in satellite and UAS imagery. The performance of studies is quantitatively compared with metrics that evaluate false positives and false negatives from automated detection against manual counts of animals, which allows for a better assessment of the impact of miscounts in conservation contexts. In general, methods that relied solely on statistical differences in the spectral responses of animals and their surroundings performed worse than studies that used convolutional neural networks (CNN). Despite mixed results, CNN showed promise, and its use and evaluation should continue. Overall, while automation can reduce time and labor, more research is needed to improve the accuracy of automated counts. With the current state of knowledge, it is best to use semi-automated approaches that involve user revision of the output. These approaches currently enable the best tradeoff between time effort and detection accuracy. Based on our analysis, we identified thermal infrared UAS imagery as a future research avenue for marine mammal detection and also recommend the further exploration of object-based image analysis (OBIA). Our analysis also showed that past studies have focused on the automated detection of baleen whales and pinnipeds and that there is a gap in studies looking at toothed whales, polar bears, sirenians, and mustelids.
海洋哺乳动物面临着多种威胁，如全球气候变化、兼捕、船只碰撞等。在这种情况下，为了为保护工作提供信息，有必要更频繁地进行空间范围更广的丰度和分布研究。海洋哺乳动物的调查已经在陆地、船只和飞机上进行了目视调查。这些方法可能成本高昂，在偏远地区进行具有后勤挑战性，对研究人员来说很危险，并且会打扰动物。越来越多地使用卫星和无人航空系统（UAS）的图像可以帮助解决其中一些挑战，补充载人调查，并允许更频繁和均匀分布的调查，特别是对于偏远地区。然而，卫星和 UAS 图像中的手动计数仍然是耗时和劳动密集型的，但图像分析的自动化提供了有希望的解决方案。在这里，我们回顾了应用于卫星和 UAS 图像中检测海洋哺乳动物的自动化方法的文献。使用评估自动检测中假阳性和假阴性的指标对研究的性能进行定量比较，这些指标可以更好地评估在保护背景下的计数错误的影响。一般来说，仅依赖于动物及其周围环境光谱响应差异的方法的性能不如使用卷积神经网络（CNN）的研究。尽管结果参差不齐，但 CNN 显示出了前景，应继续使用和评估。总体而言，自动化虽然可以减少时间和劳动力，但仍需要更多的研究来提高自动计数的准确性。在当前的知识状态下，最好使用涉及用户对输出进行修正的半自动方法。这些方法目前在时间和检测精度之间实现了最佳折衷。根据我们的分析，我们确定了热红外 UAS 图像是未来海洋哺乳动物检测的研究方向，并建议进一步探索基于对象的图像分析（OBIA）。我们的分析还表明，过去的研究集中在须鲸和鳍足类动物的自动检测上，而在研究齿鲸、北极熊、海牛目动物和鼬科动物方面存在差距。
