Canadian Journal of Remote Sensing ( IF 2.6 ) Pub Date : 2021-04-24 , DOI: 10.1080/07038992.2021.1910499 Nariman Firoozy 1 , Nicholas Sandirasegaram 1
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.
中文翻译:
通过深度卷积生成对抗网络解决 SAR 图像船舶分类失衡
摘要
航运占世界贸易的大部分,合成孔径雷达 (SAR) 图像是主要的非自愿全条件船舶监控和分类方法。然而,用于深度学习的大型 SAR 数据集难以管理,通常会导致类别不平衡。在这里,加权成本函数或过采样等传统方法被证明不足以满足我们的应用。因此,我们建议利用深度卷积生成对抗网络 (DCGAN) 对少数类进行训练,并生成新的 SAR 芯片来平衡训练数据集。因此提出了一个利用我们的方法的案例研究,并设计了一个基分类器来评估其性能。