Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong 999077, China.
Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong 999077, China.
Sensors (Basel). 2022 Jun 24;22(13):4769. doi: 10.3390/s22134769.
This paper proposes a novel inverse method based on the deep convolutional neural network (ConvNet) to extract snow's layer thickness and temperature via passive microwave remote sensing (PMRS). The proposed ConvNet is trained using simulated data obtained through conventional computational electromagnetic methods. Compared with the traditional inverse method, the trained ConvNet can predict the result with higher accuracy. Besides, the proposed method has a strong tolerance for noise. The proposed ConvNet composes three pairs of convolutional and activation layers with one additional fully connected layer to realize regression, i.e., the inversion of snow parameters. The feasibility of the proposed method in learning the inversion of snow parameters is validated by numerical examples. The inversion results indicate that the correlation coefficient (R2) ratio between the proposed ConvNet and conventional methods reaches 4.8, while the ratio for the root mean square error (RMSE) is only 0.18. Hence, the proposed method experiments with a novel path to improve the inversion of passive microwave remote sensing through deep learning approaches.
本文提出了一种基于深度卷积神经网络（ConvNet）的新反演方法，通过被动微波遥感（PMRS）提取雪的层厚和温度。所提出的 ConvNet 是使用通过传统计算电磁方法获得的模拟数据进行训练的。与传统的反演方法相比，经过训练的 ConvNet 可以更准确地预测结果。此外，所提出的方法对噪声具有很强的容忍度。所提出的 ConvNet 由三个卷积和激活层对组成，外加一个全连接层，以实现回归，即雪参数的反演。数值示例验证了所提出的方法在学习雪参数反演方面的可行性。反演结果表明，所提出的 ConvNet 与传统方法之间的相关系数（R2）之比达到 4.8，而均方根误差（RMSE）的比值仅为 0.18。因此，该方法通过深度学习方法尝试了一种新的途径来改进被动微波遥感的反演。
