Cloud Detection of ZY-3 Satellite Remote Sensing Images Based on Deep Learning

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 arc input into the support vector machine classifier to obtain the cloud detection results. The typical regions arc selected for cloud detection experiments, and the detection results arc 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.