针对高分六号WFV数据应用研究基础相对薄弱,准噶尔山楂遥感识别在算法、数据源等方面存在不足的问题,联合GF-6 WFV和ZY-3号影像数据提取多种分类特征,基于面向对象分割、特征选择、特征重要性评价与组合以及多分类器联合等方法对准噶尔山楂的遥感识别开展研究,提出优选特征辅助下的面向对象多分类器组合的准噶尔山楂识别算法。研究表明：（1） GF-6 WFV数据能够很好的对准噶尔山楂进行识别,特别是新增的红边波段对树种识别具有重要作用;（2） 面向对象分割、特征选择和多特征组合都对准噶尔山楂识别的精度具有正向提升作用;（3） 多分类器组合算法能够弥补单一分类器表征力差异造成的误差,显著提高识别精度和算法的稳定性。

Our ability to extract spatial distribution information for Crataegus songarica by remote sensing is relatively weak, and the performance of a single algorithm in information extraction is different. To address this problem, we explored the application potential of domestic Gaofen-6 (GF-6) wide-field-view (WFV) data in tree species recognition in arid and semi-arid areas. Using GF-6 WFV and Ziyuan-3 (ZY-3) data, we constructed a combined classifier recognition algorithm with multi-feature assistance. First, we combined a linear spectral clustering algorithm for ZY-3 data super-pixel segmentation with the sample set to determine the optimal scale, avoid the salt-and-pepper phenomenon and improve the recognition accuracy. Second, we extracted spectral features, texture features, and vegetation index features based on multi-source data, using the recursive feature elimination method to select classification features. Feature importance was evaluated based on the mean decrease impurity to construct the optimal classification feature set, improve the separability between feature classes, and reduce the redundancy between features. Finally, we constructed a weight-adaptive voting combination classifier based on the support vector machine algorithm and random forest algorithm. Combined with a variety of classification schemes, the spatial distribution of Crataegus songarica was extracted and verified to evaluate the influence of object-oriented algorithm, multi-classifier combination algorithm, and feature selection on the classification accuracy. The results showed that GF-6 WFV data can be used to identify Crataegus songarica and have great application potential in forestry production. Compared with GF-1 WFV, two new red-edge bands of GF-6 WFV play an important role in the identification of Crataegus songarica. Compared with traditional pixel-based methods, the object-based recognition of Crataegus songarica effectively mitigated the pepper salt phenomenon and significantly improved the recognition accuracy, algorithm efficiency, and robustness. Moreover, the feature selection effectively reduced the redundancy between classification features and improved the computational efficiency and algorithm stability; the selected features significantly improved the accuracy. Using a weight-adaptive multi-classifier voting combination algorithm can integrate the advantages of different algorithms, effectively avoid the partial ‘confusion’ caused by the representation force difference of the single classifier, and improve the recognition accuracy.