MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Res

MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images

There are limited studies on the semantic segmentation of high-resolution Polarimetric Synthetic Aperture Radar (PolSAR) images due to the scarcity of training data and the inference of speckle noises. The Gaofen contest has provided open access of a high-quality PolSAR semantic segmentation dataset.Taking this chance, we propose a Multi-path ResNet (MP-ResNet) architecture for the semantic segmentation of high-resolution PolSAR images. Compared to conventional U-shape encoder-decoder convolutional neural network (CNN) architectures, the MP-ResNet learns semantic context with its parallel multi-scale branches, which greatly enlarges its valid receptive fields and improves the embedding of local discriminative features. In addition, MP-ResNet adopts a multi-level feature fusion design in its decoder to make the best use of the features learned from its different branches. Ablation studies show that the MPResNet has significant advantages over its baseline method (FCN with ResNet34). It also surpasses several classic state-of-the-art methods in terms of overall accuracy (OA), mean F1 and fwIoU, whereas its computational costs are not much increased. This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images. The code is available at: https://github.com/ggsDing/SARSeg.

MP-ResNet：用于高分辨率PolSAR图像语义分割的多路径残差网络

由于训练数据的缺乏和斑点噪声的推断，对高分辨率极化合成孔径雷达（PolSAR）图像的语义分割的研究很少。高分竞赛为高质量PolSAR语义分割数据集提供了开放访问。.. 借此机会，我们提出了一种用于高分辨率PolSAR图像语义分割的多路径ResNet（MP-ResNet）体系结构。与传统的U形编解码器卷积神经网络（CNN）架构相比，MP-ResNet通过并行的多尺度分支来学习语义上下文，从而极大地扩展了其有效的接受域并改善了本地判别特征的嵌入。此外，MP-ResNet在其解码器中采用了多级功能融合设计，以充分利用从其不同分支中学到的功能。消融研究表明，MPResNet比其基线方法（带有ResNet34的FCN）具有明显的优势。在总体准确度（OA），均值F1和fwIoU方面，它还超过了几种经典的最新方法，而其计算成本并没有增加太多。该CNN体系结构可以用作将来对PolSAR图像的语义分割进行研究的基准方法。该代码位于：https：//github.com/ggsDing/SARSeg。 （阅读更多）