###### Abstract  
Segmenting building areas from synthetic aperture radar (SAR) images holds significant research value and practical application potential. However, the complexity of the environment, the diversity of building shapes, and the interference from speckle noise have made building area segmentation from SAR images a challenging research topic. Compared to traditional methods, deep learning-driven approaches exhibit superiority in the aspect of stability and efficiency. Currently, most segmentation methods use a single neural network to encode SAR images, then decode them through interpolation or transpose convolution operations, and finally obtain the segmented building area images using a loss function. Although effective, the methods result in the loss of detailed information and do not fully extract the deep-level features of building areas. Therefore, we propose an innovative network named PSANet. First, two sets of deep-level features of building areas were extracted using ResNet-18 and ResNet-34, with five encoded features of varying scales obtained through a fusion algorithm. Meanwhile, information on the deepest-level encoded features was enriched utilizing an atrous spatial pyramid pooling module. Next, the encoded features were reconstructed through skip connections and transposed convolution operations to obtain discriminative features of the building areas. Finally, the model was optimized using the combined CE-Dice loss function to achieve superior performance. The experimental results of the SAR images from regions with different geographical characteristics demonstrate that the proposed PSANet outperforms several recent State-of-the-Art methods. building area; semantic segmentation; multi-scale feature; synthetic aperture radar; attention mechanism +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-0000-00000-0000-00000-00000-00000-00000-00000-000000-000000-000000-0000000-000000000  
characteristics allow SAR to operate independently of factors like lighting conditions and cloud cover, ensuring functionality in all weather conditions. This capability makes SAR an essential tool in remote sensing applications. However, SAR images differ significantly from optical images in terms of their imaging mechanism, geometric characteristics [8], radiometric properties, and so on. These differences make information extraction from SAR images more complex, thereby making semantic segmentation on SAR images more challenging than on optical images. To achieve precise building area segmentation, numerous studies have been conducted. Prior to the widespread adoption of deep learning technologies, researchers mainly utilized four traditional methods for this task. The first method, based on threshold segmentation [9], distinguishes between building areas and the background by setting an appropriate intensity threshold. This method is simple and computationally efficient, but it is only suitable for situations with a significant contrast between building areas and the background and performs poorly in extracting complex background images. The second method uses edge detection [10] to extract edge information from building areas, thereby obtaining their contours. However, the extraction process is susceptible to noise interference, which can result in unclear contours of building areas. The third method employs the region-growing method [11], starting from one or more seed pixels in the images and gradually adding neighboring pixels with similar characteristics until all pixels are classified into the corresponding regions. While this method can effectively handle clear and uniform building areas, inappropriate seed points may lead to inaccurate region growth, which affects the final result.