SAR Ship Target Recognition via Selective Feature Discrimination and Multifeature Center Classifier

TL;DR

This paper introduces a novel SAR ship recognition method that uses selective feature discrimination and multiple feature centers to handle large inner-class variance and small interclass differences, improving accuracy especially with limited training data.

Contribution

It proposes a selective feature discrimination technique and a multifeature center classifier to enhance SAR ship recognition by better handling intra-class variance.

Findings

Achieved superior recognition performance on OpenSARShip and FUSAR-Ship datasets.

Effective with decreasing training samples.

Outperforms existing methods in accuracy.

Abstract

Maritime surveillance is not only necessary for every country, such as in maritime safeguarding and fishing controls, but also plays an essential role in international fields, such as in rescue support and illegal immigration control. Most of the existing automatic target recognition (ATR) methods directly send the extracted whole features of SAR ships into one classifier. The classifiers of most methods only assign one feature center to each class. However, the characteristics of SAR ship images, large inner-class variance, and small interclass difference lead to the whole features containing useless partial features and a single feature center for each class in the classifier failing with large inner-class variance. We proposes a SAR ship target recognition method via selective feature discrimination and multifeature center classifier. The selective feature discrimination automatically finds the similar partial features from the most similar interclass image pairs and the dissimilar partial features from the most dissimilar inner-class image pairs. It then provides a loss to enhance these partial features with more interclass separability. Motivated by divide and conquer, the multifeature center classifier assigns multiple learnable feature centers for each ship class. In this way, the multifeature centers divide the large inner-class variance into several smaller variances and conquered by combining all feature centers of one ship class. Finally, the probability distribution over all feature centers is considered comprehensively to achieve an accurate recognition of SAR ship images. The ablation experiments and experimental results on OpenSARShip and FUSAR-Ship datasets show that our method has achieved superior recognition performance under decreasing training SAR ship samples.