A Novel Unsupervised Evaluation Metric Based on Heterogeneity Features for SAR Image Segmentation.

The segmentation of synthetic aperture radar (SAR) images is vital and fundamental in SAR image processing, so evaluating segmentation results without ground truth (GT) is an essential part in segmentation algorithms comparison, parameters selection, and optimization. In this study, we first extracted the heterogeneous features (HF) of SAR images to adequately describe the SAR image targets, which were extracted by the proposed intensity feature extractor (IFEE) based on edge-hold and two fruitful methods. Then we proposed a novel and effective unsupervised evaluation (UE) metric G to evaluate the SAR image segmentation results, which is based on HF and uses the global intrasegment homogeneity (GHO), global intersegment heterogeneity (GHE), and edge validity index (EVI) as local segmentation measures. The effectiveness of GHO, GHE, EVI, and G was revealed by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. In experiments, four segmentation algorithms are used to segment plenty of synthetic and real SAR images as the evaluation objects, and four widely used metrics are utilized for comparison. The results show the effectiveness and superiority of the proposed metric. Moreover, the mean correlation between the proposed UE metric and the SE metric is more than 0.67 and 0.99, which indicates that the proposed metric helps in choosing parameters of segmentation algorithms without GT.