Abstract: The sparse distribution and small leaf area of desert vegetation lead to the weak vegetation spectral characteristics in the image, which makes the classification difficult. In order to improve the accuracy of desert vegetation classification, the Gurbantunggut Desert was selected as the research area, the remote sensing image and digital surface model of drones were used as data sources, and the object-oriented random forest algorithm was used to enhance the basis of decorrelation stretched spectral information, and then analyze the change of classification accuracy before and after the correlation stretching. The results showed that: the overall classification accuracy of slightly, moderately and seriusly desertified areas based on decorrelation stretching combined with object-oriented and random forest algorithm was 91.01%, 95.34% and 93.18%, respectively, which was 19.94%, 16.10% and 17.61% higher than the original image classification accuracy. This study realized the high-precision classification of desert vegetation, and provided reference for obtaining the distribution of desert vegetation and desertification monitoring test.

Key words: UAV remote sensing, object-oriented, decorrelation stretch, random forest algorithm, desert vegetation classification

摘要： 荒漠植被分布稀疏且叶面普遍较小，导致影像中植被光谱特征较弱，分类难度较大。为提高荒漠植被分类精度，选取古尔班通古特沙漠为研究区，以无人机遥感影像和数字表面模型为数据源，采用面向对象的随机森林算法，在去相关拉伸光谱信息增强基础上对荒漠植被进行分类，分析去相关拉伸前后分类精度的变化。结果表明：基于去相关拉伸并结合面向对象和随机森林算法的轻、中、重度沙漠化区总体分类精度分别为91.01%、95.34%、93.18%，较原始影像分类精度分别提升19.94%、16.10%、17.61%，实现了对荒漠植被的高精度分类，从而为获取荒漠区植被分布状况以及荒漠化监测提供参考。

关键词: 无人机遥感, 面向对象, 去相关拉伸, 随机森林算法, 荒漠植被分类