An Efficient Gabor Feature-Based Multi-task Joint Support Vector Machines Framework for Hyperspectral Image Classification

摘要：

　　In this paper,a novel multi-task learning(MTL)framework for a series of Gabor features via joint probabilistic outputs of support vector machines(SVM),abbreviated as GF-MTJSVM,has been proposed for Hyperspectral image(HSI)classification.Specifically,we firstly use a series of Gabor wavelet filters with different scales and frequencies to extract spectral-spatial-combined features from the HSI data.Then,we apply these Gabor features into the multi-task learning framework via joint probabilistic outputs of SVM.Experimental results on two widely used real HSI data indicate that the proposed GF-MTJSVM approach outperforms several well-known classification methods.

关键词： Hyperspectral image classification Multi-task support vector machines Gabor features

作者: Sen Jia Bin Deng

作者单位: College of Computer Science and Software Engineering,Shenzhen University,Shenzhen,China

会议类型: 国际会议

会议名称: 第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)

会议地点: 成都

会议语种:英文

页码: 14-25

在线出版日期: 2016-11-03（万方平台首次上网日期，不代表论文的发表时间）