Semantic segmentation is a key technology for remote sensing image analysis widely used in land cover classification, natural disaster monitoring, and other fields. Unlike traditional image segmentation, there are various targets in remote sensing images, with a large feature difference between the targets. As a result, segmentation is more difficult, and the existing models retain low accuracy and inaccurate edge segmentation when used in remote sensing images. This paper proposes a multi-attention-based semantic segmentation network for remote sensing images in order to address these problems. Specifically, we choose UNet as the baseline model, using a coordinate attention-based residual network in the encoder to improve the extraction capability of the backbone network for fine-grained features. We use a content-aware reorganization module in the decoder to replace the traditional upsampling operator to improve the network information extraction capability, and, in addition, we propose a fused attention module for feature map fusion after upsampling, aiming to solve the multi-scale problem. We evaluate our proposed model on the WHDLD dataset and our self-labeled Lu County dataset. The model achieved an mIOU of 63.27% and 72.83%, and an mPA of 74.86% and 84.72%, respectively. Through comparison and confusion matrix analysis, our model outperformed commonly used benchmark models on both datasets.

The microwave backscattering model is one of the most effective tools for surface soil moisture (SSM) inversion, which has strong theoretical support, but the inverse problem is difficult to solve. Advance in artificial intelligence offers possibilities to learn complex nonlinear relationships in a data-driven way, but it lacks physical mechanism. To combine the advantages of model-driven and data-driven methods, an SSM inversion approach that couples the AIEM-Oh model with Deep neural networks (DNNs) was proposed in this study. DNNs with different inputs were trained with a large number of simulation data generated from the AIEM-Oh model, thus embedding physical mechanisms in the data-driven scheme. Two field experiments at different scales were carried out to evaluate the performances of the proposed approach over bare surfaces. The effects of polarization modes and prior knowledge of surface roughness on SSM inversion were explored, and the accuracy of the approach was compared with the existing methods. The results suggest that satisfactory accuracy was obtained by the proposed approach, the RMSE between the measured and estimated values of SSM was 0.03-0.04 cm3·cm-3 with prior knowledge of soil roughness, and the RMSE was 0.08-0.10 cm3·cm-3 without the prior soil roughness information. VV polarization was more sensitive to SSM over bare surfaces than VH polarization. Moreover, the approach showed stable performance in different experimental regions. The results demonstrate the capability and reliability of the coupled approach for SSM inversion over bare surfaces.