Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial Imagery
Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial Imagery
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DOI：
10.3390/rs10091487
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Weixing Zhang;C. Witharana;A. Liljedahl;M. Kanevskiy
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作者：
Weixing Zhang;C. Witharana;A. Liljedahl;M. Kanevskiy
由于微地形与水的流动和储存、植被演替和永久冻土动力学之间的联系，与冰楔多边形有关的微地形控制着北极生态系统、永久冻土和水文动力学的许多方面，从地方到区域。大范围的冰楔退化正在以惊人的速度将低中心的多边形转变为高中心的多边形。尽管有亚米尺度的遥感图像，但尚无关于泛北极尺度上的冰楔多边形空间分布的准确数据。这是因为必要的空间细节很快就会产生数据量，阻碍了大范围地理范围内的手动和半自动测绘方法。因此，要将大图像转换为“科学就绪”的有洞察力的分析，需要先进的机器学习技术和高性能计算资源提供支持的新颖的图像到评估管道。在这项探索性研究中，我们使用了一种深度学习驱动的对象实例分割方法(即MASK R-CNN)来对非常高空间分辨率的航空正射影像中的冰楔多边形进行圈定和分类。我们在北阿拉斯加的努伊克苏特附近进行了一项系统的实验，以衡量MaskR-CNN在空间分辨率(0.15m到1m)和图像场景内容(总计134 km2)上的性能和互操作性。训练过的MASK R-CNN报告的平均精度分别为0.70和0.60，阈值分别为0.50和0.75。人工验证表明，约95%的单个冰楔多边形被正确划定和分类，总体分类准确率为79%。我们的发现表明，MASK R-CNN是一种从高分辨率光学图像中自动识别冰楔多边形的稳健方法。总体而言，这种自动成像的密集测绘方法可以提供一个基础框架，可能会推动未来泛北极关于永久冻土融化、冻土地带景观演变以及高纬度在全球气候系统中的作用的研究。
The microtopography associated with ice-wedge polygons governs many aspects of Arctic ecosystem, permafrost, and hydrologic dynamics from local to regional scales owing to the linkages between microtopography and the flow and storage of water, vegetation succession, and permafrost dynamics. Wide-spread ice-wedge degradation is transforming low-centered polygons into high-centered polygons at an alarming rate. Accurate data on spatial distribution of ice-wedge polygons at a pan-Arctic scale are not yet available, despite the availability of sub-meter-scale remote sensing imagery. This is because the necessary spatial detail quickly produces data volumes that hamper both manual and semi-automated mapping approaches across large geographical extents. Accordingly, transforming big imagery into ‘science-ready’ insightful analytics demands novel image-to-assessment pipelines that are fueled by advanced machine learning techniques and high-performance computational resources. In this exploratory study, we tasked a deep-learning driven object instance segmentation method (i.e., the Mask R-CNN) with delineating and classifying ice-wedge polygons in very high spatial resolution aerial orthoimagery. We conducted a systematic experiment to gauge the performances and interoperability of the Mask R-CNN across spatial resolutions (0.15 m to 1 m) and image scene contents (a total of 134 km2) near Nuiqsut, Northern Alaska. The trained Mask R-CNN reported mean average precisions of 0.70 and 0.60 at thresholds of 0.50 and 0.75, respectively. Manual validations showed that approximately 95% of individual ice-wedge polygons were correctly delineated and classified, with an overall classification accuracy of 79%. Our findings show that the Mask R-CNN is a robust method to automatically identify ice-wedge polygons from fine-resolution optical imagery. Overall, this automated imagery-enabled intense mapping approach can provide a foundational framework that may propel future pan-Arctic studies of permafrost thaw, tundra landscape evolution, and the role of high latitudes in the global climate system.