Detection and Recognition of SAR Small Ship Objects Using Deep Neural Network

深度神经网络下的SAR舰船目标检测与区分模型

¹ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
² Shanghai Institute of Satellite Engineering, Shanghai 200240, China

Received: 31 May 2018

Abstract

Synthetic aperture radar(SAR) ship target detection plays an increasingly important role in marine monitoring. Aimed at the problems of recognizing small size of ship targets in SAR images and the inability of traditional methods to extract fine target features due to external disturbances, we propose an improved SAR small target detection model based on the deep learning technology. The proposed model mainly consists of two parts:region proposal network(RPN) and object detection network. Firstly, a CNN model is designed and trained to accurately identify small ship targets. Then, the model is used to initialize the parameters of the shared feature extraction layer. Last, we train the proposed object detection model using a self-collected Sentinel-1 SAR small target dataset. The experimental results show that the proposed target detection model has better detection and recognition performance and anti-interference ability for small ship scalable targets in SAR images, and has certain reference value for the research of small target detection in SAR images.

摘要

合成孔径雷达（SAR）舰船目标检测在海洋监测中发挥着越来越重要的作用。针对SAR图像中舰船目标尺寸较小，传统方法易受外部干扰无法提取精细目标特征等问题，基于深度学习技术提出一种改进的SAR图像舰船小目标检测模型，主要由候选区域提取网络（RPN）和目标检测网络组成。首先设计并训练一个能精确识别舰船小目标的CNN模型，然后利用该模型对目标检测模型共享特征提取层进行参数初始化，最后利用自采集的Sentinel-1 SAR图像舰船小目标数据集对其进行训练。实验结果表明，提出的目标检测模型对SAR图像中舰船弱小比例目标有较好的检测区分性能和抗干扰能力，对SAR图像小目标检测领域研究具有一定的参考价值。