📋 RFI Segmentation Model
This folder contains inference model for binary segmentation of radio-frequency interference (RFI) in range-compressed SAR data, including:
- An unrestricted "U-Net" model used as the main or teacher model
- A lightweight "UNetSmall" model for faster deployment
- Smaller student variants and scratch training for ablation studies
These models were trained based on opensar-insight/rfi-detection-dataset.
🧩 Detailed List of Models
The main models available are:
- Unrestricted teacher model: BS64_lr3e-5_focal0.6_rfi_unrestricted.pth
- Lightweight student model: focal_lr1e-4_bs4_kdfeature_rfi_lightweight.pth
Further variants are available as part of ablation studies:
- Lightweight model trained from scratch: ablation_scratch_rfi_lightweight.pth
- Lightweight model with 6 base channels: ablation_basechannel6_rfi_lightweight.pth
- Lightweight model with 8 base channels: ablation_basechannel8_rfi_lightweight.pth
🔄 Input and Output
The input is a 4-channel SAR tensor built from VV/VH complex range-compressed data:
The output is a single-channel binary mask for the corresponding focused L1 SLC.
💻 Codebase
The full codebase, which includes training and inference capabalities, as well as script for further dataset generation, will soon be made open-source.
