电子学报2018，Vol.46Issue(1)：175-184,10.DOI:10.3969/j.issn.0372-2112.2018.01.024
联合核稀疏多元逻辑回归和TV-L1错误剔除的高光谱图像分类算法
Hyperspectral Image Classification Combining Kernel Sparse Multinomial Logistic Regression and TV-L1 Error Rejection
摘要
Abstract
Sparse multinomial logistic regression(SMLR) is an important supervised classification method for hyperspectral images(HSI).However,because the traditional SMLR based pixel-wise classifiers only use the spectral signatures,the good robustness and high classification accuracy are hardly achieved with a small number of samples without considering the spatial information of HSI.By using the kernel tricks,the kernel sparse multinomial logistic regression(KSMLR) method can partly overcome this limitation,however the resulted misclassification errors are still expected to be further reduced.According to the statistical analysis of classification errors resulted in KSMLR,we propose a novel two stage framework which combines KSMLR and error rejection for HSI classification.The proposed model,named KSMRL-TVL1,adopts the L1 norm to measure the heavy-tailed property of the classification errors so as to build the data fidelity term,and uses the total variation (TV) regularization term to measure the local spatial smoothness of the hidden probability field.The experiments on Indian Pines dataset and University of Pavia dataset show that the proposed method can better improve the robustness and classification accuracy.关键词
高光谱/图像分类/核稀疏多元逻辑回归/错误剔除Key words
hyperspectral image/image classification/kernel sparse multinomial logistic regression/error rejection分类
信息技术与安全科学引用本文复制引用
徐金环,沈煜,刘鹏飞,肖亮..联合核稀疏多元逻辑回归和TV-L1错误剔除的高光谱图像分类算法[J].电子学报,2018,46(1):175-184,10.基金项目
国家自然科学基金(No.61571230) （No.61571230）
国家重点研发计划(No.2016YFF0103604) （No.2016YFF0103604）
江苏省自然科学基金(No.BK20161500) （No.BK20161500）
江苏省333工程(No.BRA2015345) （No.BRA2015345）
