###### Abstract
The fourth method is feature extraction [12], which involves extracting key features from images using manually designed algorithms and then classifying these features with traditional machine learning methods, such as support vector machines [13] and random forest [14]. Although this method performs well in terms of accuracy and interpretability, feature design is highly dependent on the researcher's experience, and different scenarios may require different feature designs, which demand considerable time and effort. As artificial intelligence evolves, deep learning-based building area segmentation methods have significantly reduced the need for manual intervention and improved segmentation efficiency. As a result, an increasing number of researchers are dedicated to designing deep learning-based architectures for building area segmentation. However, due to the impact of geometric distortions and speckle noise in SAR images, most building area segmentation methods are primarily optimized for optical images, while methods for SAR images are relatively scarce. So far, building area segmentation from SAR images mainly relies on encoder-decoder architecture. The main idea behind this architecture is to gradually extract deep-level features of building areas using the encoder, subsequently using the decoder to transform the features into pixel-level segmentation results. [PERSON] et al. applied the classic encoder-decoder U-Net architecture to segment building areas in SAR images, achieving decent segmentation results [15]. [PERSON] et al. used an encoder with a selective spatial pyramid dilation structure and a dual-stage decoder, effectively showcasing details of large-scale building areas [16]. [PERSON] et al. designed an encoder-decoder architecture combined with attention mechanisms to enhance the distinction between building areas and background, effectively locating building areas in SAR images [17]. Although these algorithms have good segmentation performance, there are also some limitations. On the one hand, these algorithms use a single convolutional neural network (CNN) for encoding SAR images during the encoding stage. However, owing to the interference of speckle noise and geometric distortions in SAR images, the single CNN struggles to effectively extract deep-level features of building areas. On the other hand, a single CNN typically uses continuous convolution and pooling operations to extract features, which can easily result in the loss of small-scale building area information. To address the shortcomings of current building area segmentation networks for SAR images, we propose an innovative encoder-decoder network. Specifically, we replaced a single neural network with an improved Pseudo-Siamese network during the encoding phase to address the limitations of the single network in feature extraction. Meanwhile, we used an atrous spatial pyramid pooling module to enrich the multi-scale information of the deepest encoded features, reducing the loss of small-scale building area information. Next, we designed a hierarchical decoder that combines the merits of transposed convolutions and skip connections to fuse the decoding features with the encoded features of the corresponding spatial size for decoding, effectively reconstructing the details of the building areas. This study makes the following main contributions:  
1. We designed an innovative network for the segmentation of building areas in SAR images, named PSANet, which extends the existing encoder-decoder architecture. Additionally, extensive experiments were conducted across regions with diverse geographical characteristics, and the outcomes indicate the advanced performance of PSANet in building area segmentation;
2. We constructed a new encoding network based on a Pseudo-Siamese structure to extract building area features from SAR images, achieving efficient feature encoding;
3. We combined the advantages of skip connections and transposed convolutions to design a hierarchical decoder.