摘要
遥感图像易受光照和气象条件等干扰因素的影响,且随着遥感设备分辨率的提高,遥感图像中出现了更多的地表细节的问题.为了提高遥感图像的场景分类的准确度,提出一种自适应权重多视角度量学习方法.首先使用多个视角下的数据特征学习具有分辨力的度量空间,使在度量空间内同类图像紧凑,异类图像尽可能地远离;然后引入权重向量,在度量学习的过程中自适应地调节各视角间的权重关系;最后利用核技巧扩展至非线性空间,更有效地挖掘隐藏于视角间的关联和互补信息.在Google和WHU-RS遥感图像数据集上的实验结果表明,该方法具有良好的分类性能,平均分类准确率分别达到90.26%和92.62%,显著优于对比的单视角和多视角分类方法.
The remote sensing images are susceptible to interference factors such as illumination and meteorological conditions,and with the improvement of the resolution of remote sensing equipment,more surface details appear in the remote sensing image.In order to improve the accuracy of remote sensing image scene classification,a self-weighted multi-view metric learning(SW-MVML)method is proposed.Firstly,data features from multiple views are used to learn a discriminative metric space,which makes the similar images compact and dissimilar images as far away as possible.Then,the weight vector is introduced to adaptively adjust the weight relationship among different views.Finally,the kernel technique is used to extend the method to non-linear space,so that the correlation and complementary information hidden between views can be effectively exploited.The experimental results on Google and WHU-RS remote sensing image datasets show that the proposed method achieves good classification performance with the average classification accuracy of 90.26%and 92.62%respectively,which is significantly better than the comparative single-view and multi-view classification methods.
作者
周国华
蒋晖
顾晓清
殷新春
Zhou Guohua;Jiang Hui;Gu Xiaoqing;Yin Xinchun(College of Information Engineering,Changzhou Institute of Industry Technology,Changzhou 213164;School of Computer Science and Artificial Intelligence,Changzhou University,Changzhou 213164;College of Information Engineering,Yangzhou University,Yangzhou 225127)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2021年第5期755-764,共10页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61472343,61806026)
江苏省自然科学基金(BK20180956)
江苏省高职院校教师专业带头人高端研修项目(2020GRGDYX059).
关键词
遥感图像场景分类
自适应权重
多视角学习
度量学习
remote sensing image classification
self-weight
multi-view learning
metric learning