Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition

2017 International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Zhang, Di^1 ; Liu, Jia^1,2 ; Heng, Wang^1 ; Ren, Kaijun^1,2 ; Song, Junqiang^1,2

School of Computer, National University of Defense Technology, Changsha, China^1

Academy of Ocean Science and Engineering, National University of Defense Technology, Changsha, China^2

关键词: Convolutional neural network;    Discriminative features;    Learning methods;    Marine surveillances;    Recognition rates;    Stationary targets;    Synthetic aperture radar (SAR) images;    Transfer learning;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/322/7/072001/pdf
DOI  :  10.1088/1757-899X/322/7/072001

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.