  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.
