论文标题
用于SAR图像语义分割的异质特征蒸馏网络
Heterogeneous Feature Distillation Network for SAR Image Semantic Segmentation
论文作者
论文摘要
由于SAR的历史和全天候成像能力，SAR（合成孔径雷达）图像的语义分割（合成孔径雷达）最近引起了人们的关注。但是，通常比其EO（电光）对应物更难分割SAR图像，因为斑点的噪音和中途停留不可避免地参与了SAR图像。为了解决这个问题，我们研究了如何介绍EO特征以帮助训练SAR分割模型，并提出了用于分割SAR图像的异质特征蒸馏网络，称为HFD-NET，SAR分割学生模型在其中从预先培训的EO分割教师模型中获得知识。在拟议的HFD-NET中，学生和教师模型都采用相同的体系结构，但参数配置不同，并且探索了异质的特征蒸馏模型，以将潜在的EO特征从教师模型转移到学生模型，然后增强学生模型的SAR图像分割的能力。此外，还探索了一个异质特征对齐模块，以汇总每个学生模型和教师模型中分割的多尺度特征。两个公共数据集的广泛实验结果表明，所提出的HFD-NET优于七个最先进的SAR图像语义分割方法。
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.
