为提高影像云识别精度,提出一种多尺度膨胀卷积深层神经网络云识别方法。结合卫星影像特征,设计云识别卷积神经网络结构,该结构包含深层特征编码模块、局部多尺度膨胀感知模块以及云区预测解码模块。首先,编码模块中通过基础卷积层获取深度特征;其次,联合多尺度膨胀卷积和池化层共同感知,每层操作连接非线性函数,以提升网络模型的表达能力;最后,云区预测解码模块中融合对应编码模块的特征,再利用L1正则化上采样算法实现端对端的像素级云识别结果。选用典型云遮挡区域影像进行云识别实验,并与Otsu算法和FCN-8S算法进行对比。结果表明,本文所提算法的检测精度较高,Kappa系数显著提升。

To improve the accuracy of cloud detection, we propose a multi-scale dilation convolutional neural network method. Combining with the characteristic of satellite images, we design the deep convolution network structure, which includes a deep-feature coding module, a local dilation perception module, and a cloud-dense decoding module. First, the deep-features of cloud are obtained by the basic convolutional layer in conjunction with the coding module. Second, multi-scale dilation convolution layers jointed with pooling layers are used to perceive corporately. A nonlinear function is employed in each block to improve the effectiveness of network model expression. Finally, the cloud-dense decoding module integrate the features corresponding to those included in the coding module and then utilize the L1 regularization upsampling algorithm to accomplish the end-to-end pixel-level cloud detection task. Cloud detection experiments are performed in the typical cloud mask areas; the results are compared with those of the Otsu algorithm and the FCN-8S method. The results indicate that the accuracy of proposed method is higher and the Kappa coefficient is effectively improved.