红外与毫米波学报2019，Vol.38Issue(3)：290-295,6.DOI:10.11972/j.issn.1001-9014.2019.03.006
CCNet:面向多光谱图像的高速船只检测级联卷积神经网络
CCNet: A high-speed cascaded convolutional neural network for ship detection with multispectral images
摘要
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.关键词
船只检测/遥感图像处理/卷积神经网络/多光谱图像Key words
ship detection/remote image processing/convolutional neural network/multispectral image分类
信息技术与安全科学引用本文复制引用
张忠星,李鸿龙,张广乾,朱文平,刘力源,刘剑,吴南健..CCNet:面向多光谱图像的高速船只检测级联卷积神经网络[J].红外与毫米波学报,2019,38(3):290-295,6.基金项目
This work is supported by The National Key Research and Development Program of China (Grant No.2016YFA0202200),National Natural Science Foundation of China (Grant Nos.61434004,61234003),National Natural Science Foundation for the Youth of China (61504141,61704167),National Key R&D Program of Beijing (Z181100008918009) and Youth Innovation Promotion Association Program,Chinese Academy of Sciences (No.2016107) （Grant No.2016YFA0202200）
