论文标题
MP-RESNET：高分辨率POLSAR图像语义分割的多路剩余网络
MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images
论文作者
论文摘要
关于训练数据稀缺和斑点噪声的推断，关于高分辨率极化合成孔径（Polsar）图像的语义分割的研究有限。高芬竞赛提供了高质量的POLSAR语义分段数据集的开放访问。借此机会，我们提出了一个多路径重新网络（MP-Resnet）架构，用于高分辨率Polsar图像的语义分割。与传统的U形编码器卷积神经网络（CNN）结构相比，MP-Resnet通过其平行的多尺度分支来学习语义上下文，从而大大扩大其有效的接收场并改善了局部歧视性特征的嵌入。此外，MP-Resnet在其解码器中采用了多层次功能融合设计，以充分利用从其不同分支中学到的功能。消融研究表明，MPRESNET比其基线方法具有显着优势（带有RESNET34的FCN）。就整体准确性（OA），平均F1和FWIOU而言，它还超过了几种经典的最先进方法，而其计算成本并不多。该CNN体系结构可以用作基线方法，用于将来研究Polsar图像的语义分割。该代码可在以下网址提供：https：//github.com/ggsding/sarseg。
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.
