华中科技大学学报（自然科学版）2017，Vol.45Issue(7)：89-93,121,6.DOI:10.13245/j.hust.170717
联合空谱信息的高光谱影像半监督ELM分类
Semi-supervised ELM combined with spectral-spatial features for hyperspectral imagery classification
摘要
Abstract
For hyperspectral imagery′s processing, the number of labeled samples is often small, and the quality of them is uneven, and there exist a large number of unlabeled samples.Aiming at this problem, a semi-supervised algorithm based on extreme learning machine for hyperspectral imagery classification was presented.Firstly, according to the theory of the graph, the undirected weighted graph was constructed, and the graph was combined with both labeled and unlabeled samples′ spectral and spatial features.Then, by considering the smoothness constraint and the structure minimization principle, the classification objective function was constructed.Finally, parameters were solved and semi-supervised classification of hyperspectral image was achieved.The experimental results show that the proved method can improve the classification accuracy effectively by using unlabeled samples′ information when the labeled samples′ size is small.关键词
高光谱影像/极限学习机/半监督学习/核方法/影像分类Key words
hyperspectral imagery/extreme learning machine/semi-supervised learning/kernel method/imagery classification分类
信息技术与安全科学引用本文复制引用
付琼莹,余旭初,张鹏强,魏祥坡..联合空谱信息的高光谱影像半监督ELM分类[J].华中科技大学学报（自然科学版）,2017,45(7):89-93,121,6.基金项目
国家自然科学基金资助项目(41501482) （41501482）
河南省科技攻关计划资助项目(15202210014) （15202210014）
地理信息工程国家重点实验室开放基金资助项目(SKLGIE2015-M-3-1,SKLGIE2015-M-3-2). （SKLGIE2015-M-3-1,SKLGIE2015-M-3-2）
