# Gaussian Dynamic Convolution for Semantic Segmentation in Remote Sensing Images
###### Abstract  
Different scales of the objects pose a great challenge for the segmentation of remote sensing images of special scenes. This paper focuses on the problem of large-scale variations of the target objects via a dynamical receptive field of the deep network. We construct a Gaussian dynamic convolution network by introducing a dynamic convolution layer to enhance remote sensing image understanding. Moreover, we propose a new Gaussian pyramid pooling (GPP) for multi-scale object segmentation. The proposed network can expand the size of the receptive field and improve its efficiency in aggregating contextual information. Experiments verify that our method outperforms the popular semantic segmentation methods on large remote sensing image datasets, including iSAID and LoveDA. Moreover, we conduct experiments to demonstrate that the Gaussian dynamic convolution works more effectively on remote sensing images than other convolutional layers.  
remote sensing; semantic segmentation; deep learning; convolutional neural networks 2022145736.12.12.  
to the dynamical receptive field of the deep network. We introduce a Gaussian dynamic convolution (GDConv) kernel which dynamically increases its receptive field by sampling different offsets via Gaussian distributions. It can effectively enhance the ability to extract features at different scales on remote sensing images. Furthermore, we construct a Gaussian dynamic convolutional network (GDCN) for remote sensing image segmentation, which makes the receptive field for extracting contextual information more flexible and richer in the extracted features. As shown in Figure 1, we use an example of the airport to illustrate the difference between GDConv and generally dilated convolution. The dilated convolution used in Figure 1a increases the receptive field; however, it only provides features at a fixed scale. In contrast, our GDConv can randomly select the center of the convolution kernel. It gives an additional offset to the convolution weight vector in each direction to expand the receptive field, which extracts features of different scales and shapes, as shown in Figure 1b.  
The main contributions of this paper are as follows:  
1. We introduce the GDConv layer to the field of semantic segmentation for high-resolution remote sensing images. It can dynamically adjust the size of the receptive field to make the extracted multi-scale features rich and vivid.
2. We construct a Gaussian pyramid pooling (GPP) module and a Gaussian dynamic convolutional network (GDCN) to obtain high accuracy of the multi-scale object segmentation.
3. The experiments show that our method obtains convincing results on both remote sensing datasets iSAID [8] and LoveDA [9], which indicates that the GDConv works well on the problems of large-scale changes and difficult shapes of the objects on the remote sensing images.  
The rest of this work will be organized as follows. In Section 2, related works are reviewed. In Section 3, we propose the Gaussian Dynamic Convolution Network. Furthermore, we present a detailed comparison in Section 4. Finally, we conclude this paper in Section 5.