# Heterogeneous Feature Distillation Network for SAR Image Semantic Segmentation

arXiv: https://arxiv.org/abs/2210.08988 · HTML: https://mlsanity.com/paper/2210.08988/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Detection and dense prediction distillation, one of 12 directions in Knowledge distillation.
- Builds on 37 of its 58 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Placed in

- Knowledge distillation / [Detection and dense prediction distillation](https://mlsanity.com/distill/detection-and-dense-prediction-distillation/)

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation](https://mlsanity.com/paper/1611.06612/) (2016)
- [SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion](https://mlsanity.com/paper/1906.07789/) (2019)
- [FaPN: Feature-aligned Pyramid Network for Dense Image Prediction](https://mlsanity.com/paper/2108.07058/) (2021)
- [AlignSeg: Feature-Aligned Segmentation Networks](https://mlsanity.com/paper/2003.00872/) (2020)
- [SpaceNet 6: Multi-Sensor All Weather Mapping Dataset](https://mlsanity.com/paper/2004.06500/) (2020)
- [Instance-Conditional Knowledge Distillation for Object Detection](https://mlsanity.com/paper/2110.12724/) (2021)
- [DenserNet: Weakly Supervised Visual Localization Using Multi-scale Feature Aggregation](https://mlsanity.com/paper/2012.02366/) (2020)
- [Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images](https://mlsanity.com/paper/2205.02049/) (2022)
- [Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2209.07088/) (2022)
- [Contrastive Multiview Coding with Electro-optics for SAR Semantic Segmentation](https://mlsanity.com/paper/2109.00120/) (2021)
- [Glacier Calving Front Segmentation Using Attention U-Net](https://mlsanity.com/paper/2101.03247/) (2021)
- [CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images](https://mlsanity.com/paper/2011.08362/) (2020)
- [Bayesian U-Net for Segmenting Glaciers in SAR Imagery](https://mlsanity.com/paper/2101.03249/) (2021)
- [Polarimetric SAR Image Semantic Segmentation with 3D Discrete Wavelet Transform and Markov Random Field](https://mlsanity.com/paper/2008.11014/) (2020)
- [Snow avalanche segmentation in SAR images with Fully Convolutional Neural Networks](https://mlsanity.com/paper/1910.05411/) (2019)
- [On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery](https://mlsanity.com/paper/2102.08312/) (2021)
- [Wide-Area Land Cover Mapping with Sentinel-1 Imagery using Deep Learning Semantic Segmentation Models](https://mlsanity.com/paper/1912.05067/) (2019)
- [Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery](https://mlsanity.com/paper/2204.04438/) (2022)
- [Lake Ice Detection from Sentinel-1 SAR with Deep Learning](https://mlsanity.com/paper/2002.07040/) (2020)

## Abstract (arXiv)

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.