Authors: Zhang, ZX; Li, HL; Zhang, GQ; Zhu, WP; Liu, LY; Liu, J; Wu, NJ

JOURNAL OF INFRARED AND MILLIMETER WAVES

Volume: 38 Issue: 3 Pages: 290-295 Published: JUN 2019 Language: English Document type: Article

DOI: 10.11972/j.issn.1001-9014.2019.03.006

Abstract:

A novel ship detection method using cascaded convolutional neural network (CCNet) with multispectral image is proposed to achieve high-speed detection. The CCNet employs two cascaded convolutional neural networks (CNN) for extracting regions of interest (ROIs) , locating and segmenting ship objects sequentially. Benefit from the abundant details of the multispectral image, CCNet can extract more robust feature for achieving more accurate detection. The efficiency of CCNet has been validated by the experiments on the SPOT 6 satellite multispectral images. Compared with the state-of-the-art deep-learning-based ship detection algorithms, the proposed ship detection algorithm accelerates the processing by more than 5 times with a higher detection accuracy.

Full Text: http://journal.sitp.ac.cn/hwyhmb/hwyhmben/ch/reader/view_abstract.aspx?flag=1&file_no=180420&journal_id=hwyhmbcn