The availability of aerial and satellite imageries has greatly reduced the costs and time associated with gully mapping, especially in remote locations. Regardless, accurate identification of gullies from satellite images remains an open issue despite the amount of literature addressing this problem. The main objective of this work was to investigate the performance of support vector machines (SVM) and random forest (RF) algorithms in extracting gullies based on two resampling methods: bootstrapping and k-fold cross-validation (CV). In order to achieve this objective, we used PlanetScope data, acquired during the wet and dry seasons. Using the Normalized Difference Vegetation Index (NDVI) and multispectral bands, we also explored the potential of the PlanetScope image in discriminating gullies from the surrounding land cover. Results revealed that gullies had significantly different (p < 0.001) spectral profiles from any other land cover class regarding all bands of the PlanetScope image, both in the wet and dry seasons. However, NDVI was not efficient in gully discrimination. Based on the overall accuracies, RF’s performance was better with CV, particularly in the dry season, where its performance was up to 4% better than the SVM’s. Nevertheless, class level metrics (omission error: 11.8%; commission error: 19%) showed that SVM combined with CV was more successful in gully extraction in the wet season. On the contrary, RF combined with bootstrapping had relatively low omission (16.4%) and commission errors (10.4%), making it the most efficient algorithm in the dry season. The estimated gully area was 88 ± 14.4 ha in the dry season and 57.2 ± 18.8 ha in the wet season. Based on the standard error (8.2 ha), the wet season was more appropriate in gully identification than the dry season, which had a slightly higher standard error (8.6 ha). For the first time, this study sheds light on the influence of these resampling techniques on the accuracy of satellite-based gully mapping. More importantly, this study provides the basis for further investigations into the accuracy of such resampling techniques, especially when using different satellite images other than the PlanetScope data.

中文翻译：

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使用 K 折交叉验证和 Bootstrapping 重采样技术在映射复杂沟壑系统示例上的分类效率

航拍和卫星图像的可用性大大降低了与沟渠测绘相关的成本和时间，尤其是在偏远地区。无论如何，尽管有大量文献解决了这个问题，但从卫星图像中准确识别沟渠仍然是一个悬而未决的问题。这项工作的主要目的是研究支持向量机 (SVM) 和随机森林 (RF) 算法在基于两种重采样方法提取沟壑时的性能：引导和 k 折交叉验证 (CV)。为了实现这一目标，我们使用了在雨季和旱季获得的 PlanetScope 数据。使用归一化差异植被指数 (NDVI) 和多光谱波段，我们还探索了 PlanetScope 图像在区分沟壑和周围土地覆盖方面的潜力。磷< 0.001) 来自任何其他土地覆盖类别的关于 PlanetScope 图像所有波段的光谱剖面，包括雨季和旱季。然而，NDVI 在沟壑识别方面效率不高。基于整体精度，RF 的 CV 性能更好，尤其是在旱季，其性能比 SVM 高 4%。尽管如此，班级指标（遗漏错误：11.8%；佣金错误：19%）表明，SVM 与 CV 相结合在雨季的沟渠提取中更成功。相反，RF结合bootstrapping的遗漏（16.4%）和委托错误（10.4%）相对较低，使其成为旱季最有效的算法。估计的沟渠面积在旱季为 88±14.4 公顷，雨季为 57.2±18.8 公顷。基于标准误差（8.2 公顷），雨季比旱季更适合识别沟壑，旱季的标准误差略高（8.6 公顷）。本研究首次阐明了这些重采样技术对基于卫星的沟渠测绘精度的影响。更重要的是，这项研究为进一步研究此类重采样技术的准确性提供了基础，尤其是在使用 PlanetScope 数据以外的不同卫星图像时。