Training Sample Selection Based on SAR Images Quality Evaluation With Multi-Indicators Fusion - Pengcheng Wang; Huanyu Liu; Junbao Li (IET Signal Processing)
In recent years, with the development of artificial neural networks, efficiently training models for synthetic aperture radar (SAR) image classification tasks has garnered significant attention from researchers. Particularly when dealing with datasets containing a large number of redundant samples, the selection of training samples becomes crucial for efficient model training. To address this, this paper proposes a SAR image quality evaluation-based training sample selection method, which integrates multiple indicators. First, a comprehensive SAR image quality evaluation index system is established, and then a SAR image quality evaluation model is constructed by combining representative quality evaluation metrics to guide sample selection. Experimental results demonstrate that the proposed method exhibits strong generalization capabilities on two datasets, MSTAR and OpenSarShip, effectively selecting efficient training samples.