摘要
为了解决最小二乘支撑矢量机无法高效地对大规模数据进行分类的问题,提出了一种空间感知矩阵学习的极化合成孔径雷达图像分类方法.感知矩阵由测量矩阵和字典的乘积构成,根据压缩感知理论,构造了空间感知矩阵.为了减小优化问题的规模,首先设计了与数据耦合的判别式测量矩阵;考虑到数据的极化信息和空间信息,构造了空间威沙特字典,减少了相干斑噪声对分类结果的影响;最后,提出了基于空间感知矩阵学习的分类器,获得了紧凑而又简洁的模型表示.真实极化合成孔径雷达数据的分类结果表明,这种分类器具有更高的分类准确率和更好的空间一致性.
To deal with the large scale problem,a classifier based on the spatial sensing matrix and Least Squares Support Vector Machine(LS-SVM)is proposed for the polarimetric synthetic aperture radar(PolSAR)image.Inspired by the compressive sensing theory,a spatial sensing matrix is designed,which is equal to the product of the measurement matrix and the kernel matrix.The discriminative sensing matrix is proposed to largely reduce the scale of the optimization problem.Then,by taking the special properties of the polarimetric data and spatial information into account,we propose a spatial-Wishart dictionary to reduce the noise of speckle.Finally,the compressive sensing inspired classifier is constructed and the sparse support vector coefficients are achieved.Classification accuracy and spatial consistency of the proposed classifier is superior to those of other classifiers.
作者
孙宸
成立业
SUN Chen;CHENG Liye(Science and Technology on Reliability Physics and Application of Electronic Component Lab.,the Fifth Electronics Research Institute of Ministry of Industry and Information Technology,Guangzhou 510610,China)
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2018年第6期92-98,共7页
Journal of Xidian University
基金
国家自然科学基金资助项目(61573267
61701361)
关键词
压缩感知
极化合成孔径雷达图像分类
判别式测量矩阵
威沙特空间核
compressive sensing
polarimetric synthetic aperture radar image classification
discriminative sensing matrix
spatial-Wishart kernel
