Aircraft Type Recognition Based on Segmentation With Deep Convolutional Neural Networks
Aircraft Type Recognition Based on Segmentation With Deep Convolutional Neural Networks
复制标题
基于深度卷积神经网络分割的飞机类型识别
DOI：
10.1109/lgrs.2017.2786232
复制
发表时间：
2018-01
影响因子：
4.8
通讯作者：
Hao Sun
中科院分区：
文献类型：
--
作者：
Jiawei Zuo;Guangluan Xu;Kun Fu;Xian Sun;Hao Sun
遥感图像中的飞机类型识别是一项很有意义的工作。由于很难获得飞机的适当代表以供承认，因此这项工作仍然具有挑战性。为了解决这个问题，我们提出了一种新的基于深度卷积神经网络的飞机类型识别框架。首先，飞机分割网络的设计，以获得精细的飞机分割结果，提供重要的细节，以区分不同的飞机。然后，提出了一个关键点检测网络来获得飞机的方向和包围盒，这是用来对齐分割结果。设计了一种新的多旋转细化方法，进一步提高了关键点的精度。最后，采用模板匹配的方法对目标进行识别，并采用交集大于并集的方法对分割结果与模板的相似度进行评价。该框架利用飞机的形状和尺度信息进行识别。实验表明，该方法优于现有的方法，在具有挑战性的数据集上可以达到95.6%的准确率。
Aircraft type recognition in remote sensing images is a meaningful task. It remains challenging due to the difficulty of obtaining appropriate representation of aircrafts for recognition. To solve this problem, we propose a novel aircraft type recognition framework based on deep convolutional neural networks. First, an aircraft segmentation network is designed to obtain refined aircraft segmentation results which provide significant details to distinguish different aircrafts. Then, a keypoints’ detection network is proposed to acquire aircrafts’ directions and bounding boxes, which are used to align the segmentation results. A new multirotation refinement method is carefully designed to further improve the keypoints’ precision. At last, we apply a template matching method to identify aircrafts, and the intersection over union is adopted to evaluate the similarity between segmentation results and templates. The proposed framework takes advantage of both shape and scale information of aircrafts for recognition. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 95.6% accuracy on the challenging data set.
登录
查看更多内容
DOI：
10.1109/icemi.2009.5274100
发表时间：
2009-10
期刊：
2009 9th International Conference on Electronic Measurement & Instruments
影响因子：
--
作者：
Donghe Wang;Xin He;Zhonghui Wei;Huilong Yu
通讯作者：
Donghe Wang;Xin He;Zhonghui Wei;Huilong Yu
DOI：
10.1109/tpami.2016.2572683
发表时间：
2017-04-01
影响因子：
23.6
作者：
Shelhamer, Evan;Long, Jonathan;Darrell, Trevor
通讯作者：
Darrell, Trevor
影响因子：
4.8
作者：
Qichang Wu;Hao Sun-;Xian Sun;Daobing Zhang;Kun Fu;Hongqi Wang
通讯作者：
Qichang Wu;Hao Sun-;Xian Sun;Daobing Zhang;Kun Fu;Hongqi Wang
DOI：
--
发表时间：
2012-06
期刊：
World Automation Congress 2012
影响因子：
--
作者：
Zixuan Fang;Guoqing Yao;Yanan Zhang
通讯作者：
Zixuan Fang;Guoqing Yao;Yanan Zhang
DOI：
10.1109/tpami.2017.2699184
发表时间：
2018-04-01
影响因子：
23.6
作者：
Chen, Liang-Chieh;Papandreou, George;Yuille, Alan L.
通讯作者：
Yuille, Alan L.