Abstract. Urban areas are complex scenarios consisting of objects with various materials. This variety poses a challenge to single-data classification schemes. In this paper, we propose a feature fusion and classification network on RGB top-view point cloud and SAR images with swin-Transformer. In this network, the heterogeneous features are learned separately by an asymmetric encoder, and then they are concatenated along the channel dimension and fed into a fusing encoder. Finally, the fused features are decoded by an UperNet for generating the semantic labels. As data we use high-resolution 3D point cloud provided by Hessigheim benchmark which are complemented by TerraSAR-X images. The overall precision and the mean intersection over union (mIoU) achieves 87.25% and 73.56%, respectively, which outperforms the single-data swin-Transformer by 4.08% and 1.91%, respectively.
The impact of barchan shape on the radar cross section is addressed in this paper. Firstly, a barchan model is introduced with the wind-blown theory and shapes of barchan models with different cycle times are compared. Then, radar cross sections are calculated by the discrete element method which bases on Kirchhoff Approximation. Monostatic and bistatic cross sections are compared and analysed when electromagnetic wave is incident to two side faces, up-wind face and lee face, respectively. Finally, the influence of barchan shape on radar cross sections is discussed.
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