欧阳颖卉,林翬,李树涛.基于卷积神经网络的光学遥感图像船只检测[J].包装工程,2016,37(15):1-6.
OUYANG Ying-hui,LIN Hui,LI Shu-tao.Convolutional Neural Network Based on Ship Detection in Optical Remote Sensing Image[J].Packaging Engineering,2016,37(15):1-6. |
| 基于卷积神经网络的光学遥感图像船只检测 |
| Convolutional Neural Network Based on Ship Detection in Optical Remote Sensing Image |
| 投稿时间：2016-05-11 修订日期：2016-08-10 |
| DOI： |
| 中文关键词: 卷积神经网络 光学遥感图像 船只检测 |
| 英文关键词: convolutional neural network optical remote sensing image ship detection |
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| 中文摘要: |
| 目的 研究无需进行复杂的图像预处理和人工特征提取，就能提高光学遥感图像的船只检测准确率和实现船只类型精细分类。方法 对输入的检测图像，采用选择性搜索的方法产生船只候选区域，用已经标记好的训练样本对卷积神经网络进行监督训练，得到网络参数，然后使用经过监督训练的卷积神经网络提取抽象特征，并对候选区域进行分类，根据船只候选区域的分类概率同时确定船只的位置以及类型。结果 与现有的2种检测方法进行对比，实验结果表明卷积神经网络能有效提高船只检测准确率，平均检测准确率达到了93.3%。结论 该检测方法无需进行复杂的预处理，能同时对船只进行检测和分类，并能有效提高船只检测准确率。 |
| 英文摘要: |
| This papers aims to improve the ship detection precision in optical remote sensing images and realize sophisticated classification of ships without need to conduct complicated pre-processing step and manual feature extraction. For input detection images, selective search method was used to generate candidate ship regions. The parameters of convolutional neural network were obtained by supervised training with labeled training data. Then the trained convolutional network was utilized to classify the candidate regions. The classification results of ship candidate regions were used to locate the ship and identify the category of ship. Compared with two existing detection methods, the experimental results indicated that the proposed convolutional neural network based method could efficiently improve the detection precision and achieve an average detection precision of 93.3%. In conclusion, the method can detect and classify ships simultaneously without complicated pre-processing step. And it also can improve the efficiency of ship detection precision as well. |
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