Impact Analysis of Incident Angle Factor on High-Resolution Sar Image Ship Classification Based on Deep Learning
Yingbo Dong, Chao Wang, Hong Zhang, Yuanyuan Wang, Bo Zhang · 2019
In this paper, a ship classification framework based on deep learning is proposed. We focus on the influence of the incident angle factor for the classification results on deep learning-based methods. A representative SAR ship dataset containing three types of ship and the coverage of incidence angle is approximately from 20° to 60° is created. We evaluated the training-test performance of four deep learning models on the dataset. Taking cargo ship as the example, the experimental results show that when using data with different range of incident angles for training, the classification performance on test set with different range of incident angles varies greatly. The first analysis of the incident angle factor in SAR ship classification using deep learning methods allowed researchers to select appropriate data when using the deep learning method to classify ships in SAR images, and may suggest satellite parameters based on the classification results.