Crevasses—cracks in glaciers and ice sheets—pose a danger to polar researchers and glaciologists. We compare the capabilities of two techniques—geomorphometric modeling and texture analysis—to recognize open and hidden crevasses using high-resolution digital elevation models (DEMs) generated from images collected by an unmanned aerial system (UAS). The first technique includes derivation of local morphometric variables; the second includes calculation of the Haralick texture features. The study area is represented by the first 30 km of a sledge route between the Progress and Vostok polar stations, East Antarctica. The UAS survey was performed by a Geoscan 201 Geodesy UAS. For the sledge route area, DEMs with resolutions of 0.25, 0.5, and 1 m were generated. Models of 12 morphometric variables and 11 texture features were derived from the DEMs. In terms of crevasse recognition, the most informative morphometric variable and texture feature was horizontal curvature and inverse difference moment, respectively. In most cases, derivation and mapping of these variables allow one to recognize crevasses wider than 3 m; narrower crevasses can be recognized for lengths from 500 m. For crevasse recognition, the geomorphometric modeling and the Haralick texture analysis can complement each other.

中文翻译：

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使用高分辨率数字高程模型识别裂缝：地貌测量建模和纹理分析的应用

裂缝——冰川和冰盖的裂缝——对极地研究人员和冰川学家构成危险。我们比较了两种技术——地貌测量建模和纹理分析——使用由无人机系统 (UAS) 收集的图像生成的高分辨率数字高程模型 (DEM) 识别开放和隐藏裂缝的能力。第一种技术包括局部形态测量变量的推导；第二个包括 Haralick 纹理特征的计算。研究区以 Progress 和 Vostok 极地站之间的雪橇路线的前 30 公里为代表，南极东部。UAS 调查由 Geoscan 201 Geodesy UAS 执行。对于雪橇路线区域，生成了分辨率为 0.25、0.5 和 1 m 的 DEM。12 个形态测量变量和 11 个纹理特征的模型来自 DEM。在裂缝识别方面，信息量最大的形态测量变量和纹理特征分别是水平曲率和反差矩。在大多数情况下，这些变量的推导和映射允许人们识别宽度超过 3 m 的裂缝；可以识别 500 m 以上的较窄裂缝。对于裂缝识别，地貌测量建模和 Haralick 纹理分析可以相辅相成。