为了实现定量化应用目标,高精度的云层检测已成为遥感数据预处理的关键步骤之一。然而,传统的云检测方法存在特征复杂、算法步骤多、鲁棒性差,且无法将高级特征和低级特征相结合的缺陷,检测效果一般。针对以上问题,提出了一种基于深度残差全卷积网络的高精度云检测方法,能够实现对遥感影像云层目标像素级别的分割。首先,编码器通过残差模块的不断降采样提取图像深层特征;然后,应用双线性插值进行上采样,结合多层次编码后的图像特征完成解码;最后,将解码后的特征图与输入图像融合后再次进行卷积,实现端到端的云检测。实验结果表明,对于Landsat 8云检测数据集,所提方法的像素精度达到93.33%,比原版U-Net提高了2.29%,比传统Otsu方法提高了7.78%。该方法可以为云层目标智能化检测研究提供有益参考。

In order to achieve the goal of quantitative application, high-precision cloud detection has become one of the key steps in remote sensing data preprocessing. However, traditional cloud detection methods have the disadvantages of complex features, multiple algorithm steps, poor robustness, inability to combine high-level features with low-level features, and ordinary detection performance. In view of the above problems, this paper proposes a high-precision cloud detection method based on deep residual fully convolutional network, which can achieve the target pixel level segmentation of cloud layer in remote sensing images. First, the encoder extracts the deep features of the image through continuous down-sampling of the residual module. Then, the bilinear interpolation is used for sampling, and the decoding is completed by combining the image features after multilevel coding. Finally, the decoded feature map is fused with the input image and convolution is performed again to achieve end-to-end cloud detection. Experimental results show that, in terms of the Landsat 8 cloud detection data set, the pixel accuracy of the proposed method reaches 93.33%, which is 2.29% higher than that of the original U-Net, and 7.78% higher than that of the traditional Otsu method. This method can provide useful reference for research on intelligent detection of cloud targets.