  Semantic segmentation for SAR (Synthetic Aperture Radar) images has attracted
increasing attention in the remote sensing community recently, due to SAR's
all-time and all-weather imaging capability. However, SAR images are generally
more difficult to be segmented than their EO (Electro-Optical) counterparts,
since speckle noises and layovers are inevitably involved in SAR images. To
address this problem, we investigate how to introduce EO features to assist the
training of a SAR-segmentation model, and propose a heterogeneous feature
distillation network for segmenting SAR images, called HFD-Net, where a
SAR-segmentation student model gains knowledge from a pre-trained
EO-segmentation teacher model. In the proposed HFD-Net, both the student and
teacher models employ an identical architecture but different parameter
configurations, and a heterogeneous feature distillation model is explored for
transferring latent EO features from the teacher model to the student model and
then enhancing the ability of the student model for SAR image segmentation. In
addition, a heterogeneous feature alignment module is explored to aggregate
multi-scale features for segmentation in each of the student model and teacher
model. Extensive experimental results on two public datasets demonstrate that
the proposed HFD-Net outperforms seven state-of-the-art SAR image semantic
segmentation methods.
