Hyperspectral Image Classification Based on Non-Parallel Support Vector Machine

Remote Sensing

Lei Fei¹  Danfeng Liu¹  Liguo Wang¹  Guangxin Liu¹  Jinghui Yang² 

[1] College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China;School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China;

关键词: hyperspectral image;    classification;    support vector machine;    non-parallel support vector machine;   

DOI  :  10.3390/rs14102447

来源: DOAJ

【 摘 要 】

Support vector machine (SVM) has a good effect in the supervised classification of hyperspectral images. In view of the shortcomings of the existing parallel structure SVM, this article proposes a non-parallel SVM model. Based on the traditional parallel boundary structure vector machine, this model adds an additional empirical risk minimization term to the original optimization problem by adding the least square term of the sample and obtains two non-parallel hyperplanes, respectively, forming a new non-parallel SVM algorithm to minimize the additional empirical risk of non-parallel SVM (Additional Empirical Risk Minimization Non-parallel Support Vector Machine, AERM-NPSVM). On the basis of AERM-NPSVM, the bias constraint is added to it, and AERM-NPSVM (BC-AERM-NPSVM) is further obtained. The experimental results show that, compared with the traditional parallel SVM model and the classical non-parallel SVM model, Twin Support Vector Machine (TWSVM), the new model, has a better effect in hyperspectral image classification and better generalization performance.

【 授权许可】

Unknown