# Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images
## 4 Experiments
### Performance  
We compared our method with several successful semantic segmentation methods, including DeepLabv3+ [4], PSPNet [3], DCN [7], FarSeg [25], and FactSeg [26]. The results are shown in Tables 1 and 2. Among them, Deeplab v3+ employs dilated convolution to build the Atrous Spatial Pyramid Pooling module. Dilated convolution is also employed by PSPNet. The problem is that it does not take full advantage of the dilated convolution to expand the receptive field range, and it does not perform well in dealing with multi-scale problems. The Deformable Convolutional Network (DCN) uses deformable convolution for feature extraction, but the disadvantage is that it introduces too much useless contextual information. Moreover, the methods of FarSeg and FactSeg are currently the most effective methods. Therefore, we choose these four methods as our comparison methods. On the iSAID dataset, our method achieves the mIoU of 64.22, which is a 4.89 improvement over DeepLabv3+ and a 3.97 improvement over PSPNet. This is because Gaussian dynamic convolution breaks the limitation of the dilation factors and can further collect irregularly shaped features. Therefore, it achieves better performance on remote sensing datasets. In classes with a large number of irregular instances, such as small vehicle (SV), helicopter (HC), and Plane, our method is significantly better than deeplabv3+ and PSPNet. Moreover, our results work well compared with the recent methods, as seen in Table 1. The results improve 0.43 mIoU over the well-known FarSeg and 0.51 mIoU over FactSeg. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **mIoU(\%)** & **Ship** & **ST** & **BD** & **TC** & **BC** & **GTF** & **Bridge** \\ \hline Deeplab v3+ [4] & 59.33 & 59.02 & 55.15 & 75.94 & 84.18 & 58.52 & 59.24 & 32.11 \\ PSPNet [3] & 60.25 & 65.2 & 52.1 & 75.7 & 85.57 & 61.12 & 60.15 & 32.46 \\ DCN [7] & 60.12 & 61.24 & 54.69 & 72.88 & 82.96 & 55.32 & 55.46 & 34.58 \\ FarSeg [25] & 63.71 & 65.38 & 61.80 & 77.73 & 86.35 & 62.08 & 56.70 & 36.70 \\ FactSeg [26] & 63.79 & 68.34 & 56.83 & 78.36 & **89.91** & 64.89 & 54.60 & 36.34 \\
**ours** & **64.22** & **69.33** & **62.2** & **78.48** & **85.59** & **65.26** & **60.28** & **37.23** \\ \hline
**Method** & **LV** & **SV** & **HC** & **SP** & **RA** & **SBF** & **Plane** & **Harbor** \\ \hline Deeplabv3+ [4] & 54.54 & 33.79 & 31.14 & 44.24 & 67.51 & 73.78 & 75.70 & 45.76 \\ PSPNet [3] & 58.03 & 42.96 & 40.89 & 46.78 & 68.6 & 71.9 & 79.5 & 54.26 \\ DCN [7] & 56.25 & 39.25 & 33.36 & 47.77 & 69.81 & 70.33 & 76.21 & 49.85 \\ FarSeg [25] & 60.59 & 46.34 & 35.82 & 51.21 & 71.35 & 72.53 & 82.03 & 53.91 \\ FactSeg [26] & **62.65** & 49.53 & 42.72 & 51.47 & 69.42 & **73.55** & 84.13 & 55.74 \\
**ours** & 61.67 & **49.85** & **42.85** & **52.01** & **69.65** & 73.25 & **84.29** & **56.71** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results (mIoU) of object segmentation on the iSAID val set. The best results are illustrated in bold in each column. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **mIoU(\%)** \\ \hline DeepLab v3+ [4] & 47.62 \\ PSPNet [3] & 48.31 \\ DCN [7] & 47.92 \\ FarSeg [25] & 48.69 \\ FactSeg [26] & 48.94 \\
**ours** & **49.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Semantic segmentation results achieved on the test set of LoveDA. Among the 15 categories provided by the iSAID dataset, we have the best results in 12 categories such as BC, ST, GTF, HC, Ship, Bridge, BD, SV, SP, RA, Plane, and Harbor. Only on three categories, TC, LV, and SBF, our results are not as good as the previous method. In the category of SBF, our method is almost the same as FactSeg with only 0.3 lower. Further analysis of the results shows that our results are 1 higher than the best method for the category with a lot of irregular boundaries such as harbor, which shows that our method has good results for handling irregular shapes in remote sensing images.