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摘要： 漓江流域是桂林山水的核心，保护漓江流域生态环境已成为国家战略。以漓江流域为研究区域，以GF-1多光谱影像和SAR影像为数据源，采用小波融合算法将GF-1多光谱影像和SAR VV极化的后向散射影像进行影像融合，再利用随机森林算法分别对GF-1多光谱影像、GF-1和Sentinel融合影像构建典型地物高精度识别模型，提取与漓江流域生态环境紧密相关的河流、针叶林、阔叶林、水田、旱地以及居民地等地物类型。研究结果表明：①在95%置信区间内，基于GF-1影像分类的总体分类精度达到96.15%，基于GF-1和Sentinel-1A后向散射系数的影像总体分类精度达到了94.40%；②河流、阔叶林和旱地在基于GF-1多光谱影像的分类精度中分别达到了97.74%、93.20%、90.90%，比基于融合GF-1多光谱和SAR的数据分别高出7.57%、8.96%和1.22%，其余地物类型两者分类精度相近；③GF-1多光谱和SAR数据的融合中，利用了小波变换进行图像融合，发现融合图像的喀斯特地貌突出，增加了地物特征的差异性。 关键词： 漓江流域;  光学遥感;  合成孔径雷达;  随机森林算法;  地物遥感识别     Abstract: Lijiang River is the core of Guilin's landscape. Protecting the ecological environment of Lijiang River Basin has become a national strategy. In this paper, Lijiang River Basin was used as the research area. The GF-1 multispectral image and SAR image were used as the data source. The wavelet fusion algorithm was used to fuse the GF-1 multispectral image and the SAR VV polarized backscatter image. Using random forest algorithm to construct a high-precision recognition model for GF-1 multispectral imagery, GF-1 and sentinel fusion images. The model can extract rivers, coniferous forests, broad-leaved forests, paddy fields, drylands, residential land and other land types that are closely related to the ecological environment of the Lijiang River. The results show that ①the overall accuracy based on GF-1 image classification reaches 96.15% in the 95% confidence interval, and the overall accuracy based on GF-1 and sentinel-1A backscatter coefficient reaches 94.40%. ②The classification accuracy of rivers, broad-leaved forests and drylands based on GF-1 multispectral images reached 97.74%, 93.20%, and 90.90%. They are 7.57%, 8.96%, and 1.22% higher than those based on the fused GF-1 multispectral and SAR data, respectively. The classification accuracy of the other features is similar. ③In the fusion of GF-1 multispectral and SAR data, wavelet transform was used for image fusion. It was found that the karst topography of the fusion image was prominent, which increased the difference of the features of the ground features.

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