自适应权重多视角度量学习的遥感图像场景分类方法

Self-Weighted Multi-View Metric Learning and Its Application for Remote Sensing Image Scene Classification

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.