针对资源三号卫星影像波段少、光谱范围受限的特点,提出了基于深度学习的资源三号卫星遥感影像的云检测方法。首先,采用主成分分析非监督预训练网络结构,获得了待测遥感影像特征;其次,为减少在池化过程中影像特征信息的丢失,提出自适应池化模型,该模型能很好地挖掘影像特征信息;最后,将影像特征输入支持向量机分类器进行分类,获得了云检测结果。选取典型区域进行云检测实验,并与传统Otsu方法进行对比。结果表明:所提方法的检测精度高,且不受光谱范围的限制,可用于资源三号卫星多光谱影像和全色影像的云检测。

The cloud detection method of ZY-3 satellite remote sensing images based on deep learning is proposed to solve the problem of the images with few image bands and limited spectral range. Firstly, we obtain the feature of remote sensing images measured with the unsupervised pre-training network structure of principal component analysis. Secondly, we put forward the adaptive pooling model, which can well mine images in order to reduce the loss of image feature information in the pooling process. Finally, the image features are input into the support vector machine classifier to obtain the cloud detection results. The typical regions are selected for cloud detection experiments, and the detection results are compared with that of the traditional Otsu method. The results show that the proposed method has high detection precision and is not limited by the spectral range, and it can be used for the multi-spectral and panchromatic images cloud detection of ZY-3 satellite.