Tackling SAR Imagery Ship Classification Imbalance via Deep Convolutional Generative Adversarial Network

Abstract

Shipping constitutes the majority of the world trade, and Synthetic Aperture Radar (SAR) imagery is the primary involuntary all-condition ship monitoring and classification approach. However, large SAR datasets for deep learning are difficult to curate, usually leading to imbalanced classes. Herein, conventional methods such as weighted cost function or over-sampling are shown to be insufficient for our application. Therefore, we propose to utilize a Deep Convolutional Generative Adversarial Network (DCGAN) to be trained on the minority class, and generate new SAR chips to balance the training dataset. A case study is consequently presented that utilizes our methodology, and a base classifier is devised to evaluate its performance. The investigation of the classification metrics confirms that DCGAN is an effective alternative for tackling imbalanced ship SAR data for deep learning applications.