Snow cover in Qinghai-Tibetan plateau (QT plateau)is very important to global climate change.Because of the complex topography and high altitude,the recognition accuracies of existing snow cover products in QT plateau are significantly lower than flat areas.This paper proposed a new method of snow cover recognition for QT plateau based on deep learning.The multispectral remote sensing data from Chinese meteorological satellite FY-3A and the multiple geographic elements information are put together as the data sources,the insitu snow depth measurements and existing snow cover products are used for selecting the labeled samples.A stacked denoising auto-encoders (SDAE)network was built and trained for feature extraction and classification,this network can be used as a classifier for distinguishing the snow cover from cloud and other snow-free surface features.The recognition results are verified by snow depth data of meteorological station observations,verification results show that the recognition accuracy of this method is significantly higher than the snow product FY-3A/MULSS,which is using the same remote sensing data source FY-3A,and sl ightly higher than the widely used snow products MOD10A1 and MYD10A1,and the cloud coverage rate of this method is the lowest.According to the validation results,this method can effectively improve the accuracy of snow cover recognition,and reduce the interference of clouds.%青藏高原积雪对全球气候变化十分重要,针对已有积雪遥感判识方法中普遍采用的可见光与红外光谱数据易受复杂地形与高海拔影响,导致青藏高原地区积雪判识精度较低的问题,提出了一种基于多光谱遥感与地理信息数据特征级融合的积雪遥感判识方法：以风云三号卫星可见光与红外多光谱遥感资料与多要素地理信息作为数据源,由地面实测雪深数据与现有积雪产品交叉筛选出样本标签,构建并训练基于层叠去噪自编码器(SDAE)的特征融合与分类网络,从而有效辨识青藏高原遥感图像中的云、积雪以及无雪地表。经地面实测雪深数据验证,该方法分类精度显著高于使用相同数据源的 FY-3A/MULSS积雪产品,略高于国际主流积雪产品MOD10A1与MYD10A1,并且年均云覆盖率最低。试验结果表明该方法可有效地减少云层对积雪判识的干扰,提升分类精度。
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