Extraction of built-up areas from polarimetric synthetic aperture radar (PolSAR) images plays a crucial role in disaster management. The polarimetric orientation angles (POAs) of built-up areas exhibit diversity, and built-up areas with POA close to 45° are often misclassified as vegetation. To address this problem, a polarimetric feature suitable for the extraction of built-up areas with large POAs is first designed, and a mixed real-complex-valued polarimetric feature combination is constructed. Then, a real-complex and spatial feature fusion classification network (RCSFFCNet) is designed. In which the proposed mixed real-complex-valued residual structure can efficiently extract mixed numerical features. Additionally, a multi-local spatial convolutional attention module is designed and embedded to efficiently fuse mixed numerical features, as well as superpixel multi-local spatial features. Experiments were conducted using PolSAR images from Gaofen-3, Radarsat-2, and ALOS-2/PALSAR-2. The experimental results show that the feature combination proposed in this paper increases the F1 score of built-up areas by approximately 2%-3%, and the F1 score of built-up areas extracted using the RCSFFCNet also improves by about 2%-3%, with F1 scores exceeding 95%. On all three datasets, the proposed method achieves the best performance in extracting built-up areas with various POAs, indicating overall superiority from feature selection to model implementation.

中文翻译：

---

使用实复极化特征和特征融合分类网络提取 PolSAR 图像中的建成区域


从极化合成孔径雷达 (PolSAR) 图像中提取建成区在灾害管理中发挥着至关重要的作用。建成区的极化方位角 (POA) 表现出多样性，POA 接近 45° 的建成区经常被错误分类为植被。为了解决这个问题，首先设计了一种适合提取具有大POA的建成区的偏振特征，并构造了混合实数-复值偏振特征组合。然后，设计了一个真实复杂的空间特征融合分类网络（RCSFFCNet）。其中所提出的混合实数复值残差结构可以有效地提取混合数值特征。此外，还设计并嵌入了多局部空间卷积注意模块，以有效融合混合数值特征以及超像素多局部空间特征。使用来自 Gaufen-3、Radarsat-2 和 ALOS-2/PALSAR-2 的 PolSAR 图像进行了实验。实验结果表明，本文提出的特征组合使建成区的F1得分提高了约2%-3%，使用RCSFFCNet提取的建成区的F1得分也提高了约2%-3 %，F1分数超过95%。在所有三个数据集上，所提出的方法在提取具有各种 POA 的建成区方面均取得了最佳性能，表明从特征选择到模型实现的整体优越性。