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Snow Parameters Inversion from Passive Microwave Remote Sensing Measurements by Deep Convolutional Neural Networks
| Content Provider | MDPI |
|---|---|
| Author | Yao, Heming Zhang, Yanming Jiang, Lijun Ewe, Hong Tat Ng, Michael |
| Copyright Year | 2022 |
| Description | 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 ( |
| Starting Page | 4769 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22134769 |
| Journal | Sensors |
| Issue Number | 13 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-06-24 |
| Access Restriction | Open |
| Subject Keyword | Sensors Remote Sensing Machine Learning Deep Convolutional Neural Networks (cnns) Passive Microwave Remote Sensing (pmrs) Inversion Dense Medium Radiative Transfer (dmrt) |
| Content Type | Text |
| Resource Type | Article |
