Aircraft Detection in High-Resolution SAR Images Using Scattering Feature Information

Abstract

Accurate aircraft detection in high-resolution Synthetic Aperture Radar (SAR) images is of great significance. Aiming at the challenges of sparsity and variability for aircraft targets in SAR images, a detection algorithm based on Scattering Feature Information (SFI) enhancement and Feature Pyramid Network (FPN) is proposed. In the former stage, the SFI, being composed of Strong Scattering Point (SSP) and its corresponding scattering region distribution model, is extracted by Harris-Laplace detector and Gaussian Mixture Model (GMM). Specially, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is introduced to response to the sensitivity of the GMM to the initial values. In the detection stage, an algorithm based on FPN is applied for aircraft detection in high-resolution images. This structure combines the high-resolution information of the underlying features with the high-semantic information of the deep features, which facilitates accurate detection of the aircrafts in a scene. In addition, Logarithmic-normal Distribution based Subdivided Conversion (LDSC) is newly proposed for SAR image preprocessing. Experiments conducted on the GF-3 satellite image of 0.5 m resolution demonstrates the superiority and robustness of the proposed method.