Abstract

An algorithm for statistical analysis of aerial photography data obtained by unmanned aerial vehicles is proposed. Segmentation of multispectral images by a complex of spectral and textural features makes it possible to identify areas of historical anthropogenic impact on the natural environment. The test site was the territory of the economic district of the Pudemsky ironworks (Udmurt Republic), where the arable lands of factory peasants were located in the first half of the 19th century. The location of the arable land and its configuration were restored as a result of the transformation of historical cartographic materials from 1817–1832. At the first stage of the algorithm, it is supposed to calculate features according to multispectral survey data (Haralick’s features, NDVI index); at the second stage, it is to reduce the number of features by the principal component analysis; at the third stage, it is to segment images based on the received features by the method k-means. The initial data were the results of multispectral imaging in Green, Red, RedEdge, and near infrared (NIR) spectral ranges. The efficiency of the proposed algorithm is shown by comparing the segmentation results with reference data (historical cartographic materials and aerial photographs in the visible range).

中文翻译：

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用于确定 19 世纪人类活动对自然环境影响的多光谱调查数据统计分析算法

摘要

提出了一种无人机航拍数据统计分析算法。通过复杂的光谱和纹理特征对多光谱图像进行分割，可以识别历史人为对自然环境产生影响的区域。试验场是普德姆斯基炼铁厂（乌德穆尔特共和国）经济区的领土，19 世纪上半叶工厂农民的耕地就位于这里。由于 1817 年至 1832 年历史制图材料的转变，耕地的位置及其配置得到了恢复。在算法的第一阶段，根据多光谱调查数据（Haralick's features，NDVI index）计算特征；在第二阶段，是通过主成分分析减少特征数量；第三阶段，根据接收到的特征对图像进行分割k 表示。初始数据是绿色、红色、RedEdge 和近红外 (NIR) 光谱范围内的多光谱成像结果。通过将分割结果与参考数据（历史制图材料和可见范围内的航拍照片）进行比较，表明了所提出算法的效率。