# SFEF-Net: Scattering Feature Extraction and Fusion Network for Aircraft Detection in SAR Images

## TL;DR

This paper introduces SFEF-Net, a new network for detecting aircraft in SAR images that improves feature extraction and handles label noise.

## Contribution

The paper proposes SFEF-Net with a sparse convolution operator, a global fusion module, and a noise-robust loss for aircraft detection in SAR.

## Key findings

- SFEF-Net outperforms existing methods in aircraft detection on the SAR-AIRcraft1.0 dataset.
- The sparse convolution operator enhances discrete feature extraction without increasing parameters.
- The noise-robust loss effectively reduces the impact of label noise on detection accuracy.

## Abstract

Synthetic aperture radar (SAR) offers robust Earth observation capabilities under diverse lighting and weather conditions, making SAR-based aircraft detection crucial for various applications. However, this task presents significant challenges, including extracting discrete scattering features, mitigating interference from complex backgrounds, and handling potential label noise. To tackle these issues, we propose the scattering feature extraction and fusion network (SFEF-Net). Firstly, we proposed an innovative sparse convolution operator and applied it to feature extraction. Compared to traditional convolution, sparse convolution offers more flexible sampling positions and a larger receptive field without increasing the number of parameters, which enables SFEF-Net to better extract discrete features. Secondly, we developed the global information fusion and distribution module (GIFD) to fuse feature maps of different levels and scales. GIFD possesses the capability for global modeling, enabling the comprehensive fusion of multi-scale features and the utilization of contextual information. Additionally, we introduced a noise-robust loss to mitigate the adverse effects of label noise by reducing the weight of outliers. To assess the performance of our proposed method, we carried out comprehensive experiments utilizing the SAR-AIRcraft1.0 dataset. The experimental results demonstrate the outstanding performance of SFEF-Net.