## 6 Conclusions  
This study explores several major challenges faced in segmenting building areas from SAR images, including complex backgrounds, speckle noise interference, and the impact of geometric distortions. These factors make building area segmentation from SAR images more difficult than from optical images. Therefore, we analyzed the existing methods for building area segmentation and discussed the advantages and disadvantages of different methods. Specifically, we focused on the limitations of current encoder-decoder architecture, such as the inability to fully extract deep-level features of building areas and the tendency to lose information from small-scale building areas during encoding. To address these issues, we designed an innovative network based on the encoder-decoder architecture. First, we designed an improved Pseudo-Siamese network that uses fusion encoding algorithm to encode the extracted two sets of features at distinct levels, thereby enhancing the extraction of deep-level features of building areas. Meanwhile, information on the deepest encoded features was enriched using an atrous spatial pyramid pooling module, thereby reducing the information loss of small-scale building areas during encoding. Next, we designed a hierarchical decoder that combines the advantages of transposed convolutions and skip connections to accurately decode the encoded features. Finally, the model prediction was optimized using the combined loss function CE-Dice to achieve optimal performance. We selected four regions with different geographical characteristics from the SARBud 1.0 dataset for experiments, namely, Jiangsu Province, Anhui Province, Tibet Province, and Taiwan Province. The experimental results demonstrate the superiority of PSANet in segmenting building areas from SAR images. Semantic segmentation of SAR images is still in its developmental stage, with many aspects requiring further research and exploration. In the next phase, we will explore suitable methods to integrate the transformer architecture. Specifically, our model has limitations in building area boundary segmentation and handling high-density building areas. We plan to enhance the understanding of contextual information using the transformer architecture, as transformers are more efficient than CNNs in capturing global information, which can improve segmentation performance. Furthermore, in SAR data, noise interference is one of the key factors affecting segmentation performance. Although incorporating attention mechanisms in the model, which automatically assigns weights to strengthen important features, can improve the model's robustness to noise to some extent, combining specialized denoising methods during the data preprocessing stage--such as non-local means denoising and the CycleGAN algorithm [42]--will further enhance the model's segmentation performance.  
Conceptualization, M.L. and L.H.; methodology, M.L. and L.H.; software, M.L. and S.L.; validation, S.L. and L.H.; formal analysis, M.L. and L.H.; investigation, S.L. and L.H.; resources, M.L. and S.L.; data curation, M.L. and S.L.; writing--original draft preparation, L.H.; writing--review and editing, L.H., M.L. and S.L.; visualization, M.L. and L.H.; supervision, S.L.; project administration, M.L. and L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by project supported by the Natural Science Foundation of Hunan Province, China (Grant No. 2023J)30233), Key Laboratory of Smart Earth, China (Grant No. KF2023 YB02-03), and project supported by the Scientific Research Fund of Hunan Provincial Education Department (Grant No. 24B0451).  
Not applicable.  
The dataset used for this study is publicly available. The SARBud 1.0 can be downloaded at [[https://github.com/CAESAR-Radi/SARBuD](https://github.com/CAESAR-Radi/SARBuD)]([https://github.com/CAESAR-Radi/SARBuD](https://github.com/CAESAR-Radi/SARBuD)) (accessed on 5 October 2024).