# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
### Prevalent Methodologies  
Semantic segmentation algorithms can classify images at the pixel level, extract more detailed features [18; 19; 20; 21], and have the characteristics of high recognition rate and fast speed. Some of the semantic segmentation algorithms are performed on datasets, such as scene datasets, indoor datasets, and human and animal datasets. Compared with these datasets, the extraction of buildings in remote sensing images has the characteristics of building occlusion, complex landform, and dense target objects. Therefore, traditional semantic segmentation algorithms cannot solve these problems well, and researchers have improved the algorithm model accordingly. It is mainly aimed at the optimization of the model structure and post-processing of the model results [22]. [PERSON] et al. [23] proposed a richer convolutional feature network (Richer Convolution Features Network, RCF) to detect building edges from high-resolution remote sensing images, making full use of semantic information and detail information for edge detection. In order to solve the problem of missed detection of small objects in building extraction, [PERSON] et al. [24] used the multi-scale fusion of feature information in the decoding stage to construct a Multiscale Decoding Network (MSDNet). However, it also generates a large number of parameters, which increases the prediction time of remote sensing images. [PERSON] et al. [25] combined spatial and channel attention mechanisms with generative adversarial networks [26] to better extract remote sensing images of buildings with irregular landforms, but also increased the complexity and computational complexity of the model. In order to deal with the complex morphology of buildings and roads in remote sensing images, [PERSON] et al. [27] proposed a universal network for building and road segmentation and constructed a dense multi-convolution module to be added to the encoding stage of the network. In the process of downsampling, more detailed information is extracted, but it cannot be recovered in more detail in the decoding stage. [PERSON] et al. [28] improved the UNet network and proposed the SiUNet network, which solved the recognition problem of a large area of buildings covered with only a small area of background in remote sensing images. In this work, they empirically found that all of the present methods did not solve the scale problem well, especially for large buildings. Many points on a large roof are often wrongly classified as background even when the roof has the same color and texture. SiUNet improved the scale invariance of the algorithm for extracting buildings of different sizes from remote sensing data, as they found large buildings hinder a high performance of FCN-based methods on remote sensing building detection. Due to the rapid development of remote sensing technology, the pixel count of most remote sensing images is very high. The previous semantic segmentation algorithms for building extraction from remote sensing images mainly improve the extraction accuracy by increasing the depth of the network model. However, a large number of model parameters and calculations are also generated. The automatic extraction of buildings from high-resolution remote sensing images is very slow, consumes a lot of resources, and leads to low work efficiency. Aiming at improving the low efficiency of automatic building extraction, this paper proposes an end-to-end FRA-Net to balance the accuracy and speed of building extraction. The whole network is divided into two processes, upsampling and downsampling. In the upsampling process, the use of large convolution kernels is avoided to reduce the complexity of the model. At the same time, depthwise separable convolution [29] is introduced, and an SRM is designed to extract feature information. The information decoding using the SRM continues during the upsampling process. Since there is a lot of complex information in remote sensing images of buildings, it includes useless information.