| 高光谱遥感影像分类研究进展 |
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| 引用本文： | 杜培军,夏俊士,薛朝辉,谭琨,苏红军,鲍蕊.高光谱遥感影像分类研究进展[J].遥感学报,2016,20(2):236-256. |
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| 作者姓名： | 杜培军 夏俊士 薛朝辉 谭琨 苏红军 鲍蕊 |
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| 作者单位： | 南京大学 卫星测绘技术与应用国家测绘地理信息局重点实验室, 江苏 南京 210023;南京大学 江苏省地理信息技术重点实验室, 江苏 南京 210023,南京大学 卫星测绘技术与应用国家测绘地理信息局重点实验室, 江苏 南京 210023;南京大学 江苏省地理信息技术重点实验室, 江苏 南京 210023,南京大学 卫星测绘技术与应用国家测绘地理信息局重点实验室, 江苏 南京 210023;南京大学 江苏省地理信息技术重点实验室, 江苏 南京 210023,中国矿业大学 资源环境信息工程江苏省重点实验室, 安徽 徐州 221116,河海大学 地球科学与工程学院, 江苏 南京 210098,南京大学 卫星测绘技术与应用国家测绘地理信息局重点实验室, 江苏 南京 210023;南京大学 江苏省地理信息技术重点实验室, 江苏 南京 210023 |
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| 基金项目： | 国家自然科学基金项目(编号:41471275);江苏省杰出青年基金项目(编号:BK2012018) |
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| 摘 要： | 随着模式识别、机器学习、遥感技术等相关学科领域的发展,高光谱遥感影像分类研究取得快速进展。本文系统总结和评述了当前高光谱遥感影像分类的相关研究进展,在总结分类策略的基础上,重点从以核方法为代表的新型分类器设计、特征挖掘、空间-光谱分类、基于主动学习和半监督学习的分类、基于稀疏表达的分类、多分类器集成六个方面对高光谱影像像素级分类最新研究进行了综述。针对今后的研究方向,指出高光谱遥感影像分类一方面要适应大数据、智能化高光谱对地观测的发展前沿,继续引入机器学习领域的新理论、新方法,综合利用多源遥感数据、多维特征空间互补的优势,提高分类精度、分类器泛化能力和自动化程度;另一方面要关注高光谱遥感应用的需求,突出高光谱遥感记录精细光谱特征的优势,针对应用需求发展有效的分类方法。
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| 关 键 词： | 高光谱遥感 分类 支持向量机 特征挖掘 多分类器集成 |
| 收稿时间： | 2015/1/28 0:00:00 |
| 修稿时间： | 2015/9/6 0:00:00 |
Review of hyperspectral remote sensing image classification |
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| DU Peijun,XIA Junshi,XUE Zhaohui,TAN Kun,SU Hongjun and BAO Rui.Review of hyperspectral remote sensing image classification[J].Journal of Remote Sensing,2016,20(2):236-256. |
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| Authors: | DU Peijun XIA Junshi XUE Zhaohui TAN Kun SU Hongjun and BAO Rui |
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| Institution: | Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China;Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China,Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China;Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China,Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China;Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China,Jiangsu Provincial Key Laboratory of Resources Environment and Information Engineering, China University Of Mining and Technology, Xuzhou 221116, China,School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China and Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China;Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China |
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| Abstract: | Studies on hyperspectral remote sensing image classification have developed rapidly with the progress of related disciplines, including pattern recognition, machine learning, and remote sensing technology. This review generates a systematic summary and conducts a comprehensive evaluation of the advancements in current techniques for hyperspectral remote sensing image classification. Based on an overview of different classification schemes, we examine the recent progress in per-pixel classification algorithms for hyperspectral images from six aspects, namely, new classifier design(e.g., kernel-based methods), feature mining, spectral spatial classification, active and semi-supervised learning, sparse representation for classification, and multiple classifier systems. Future research directions are discussed as well. On the one hand, new theories and methods of machine learning should be introduced continuously into hyperspectral image classification. Moreover, multisource data and multidimensional feature spaces may improve the accuracy, generalization capability, and automation degree of a classifier. On the other hand, new classification methods should be designed in consideration of practical requirements to meet the needs of real applications and to emphasize the advantages of fine spectra in hyperspectral remote sensing. |
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| Keywords: | hyperspectral remote sensing classification support vector machine feature mining multiple classifier system |
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