SAR Image Quality Assessment: From Sample-Wise to Class-Wise

Abstract

Target recognition is the core application of radar image interpretation. In recent years, deep learning has become the mainstream solution. However, this family of methods is highly dependent on a great deal of training samples. Limited samples may lead to problems such as underfitting and poor robustness. To solve the problem, numerous generative models have been presented. The generated samples played an important role in target recognition. It is therefore needed to assess the quality of simulated images. However, few studies were performed in the preceding works. To fill the gap, a new evaluation strategy is proposed in this paper. The proposed method is composed of two schemes: the sample-wise assessment and the class-wise one. The simulated images can then be evaluated from two different perspectives. The sample-wise assessment combines the Fisher separability criterion, fuzzy comprehensive evaluation, analytic hierarchy process, and image feature extraction into a unified framework. It is used to evaluate whether the relative intensity of the speckle noise of the SAR image and the target backscattering coefficients are well simulated. Contrarily, the class-wise assessment is designed to compare the application capability of the simulated images holistically. Multiple comparative experiments are performed to verify the proposed method.