首页 | 本学科首页   官方微博 | 高级检索  
     

高光谱影像空-谱协同嵌入的地物分类算法
引用本文:黄鸿,郑新磊. 高光谱影像空-谱协同嵌入的地物分类算法[J]. 测绘学报, 2016, 45(8): 964-972. DOI: 10.11947/j.AGCS.2016.20150654
作者姓名:黄鸿  郑新磊
作者单位:重庆大学光电技术与系统教育部重点实验室, 重庆 400044
基金项目:The National Natural Science Foundation of China(41371338),The Basic and Advanced Research Program of Chongqing(cstc2013jcyjA40005),Postgraduate Research and Innovation Program of Chongqing (No.CYB15052)@@@@国家自然科学基金(41371338),重庆市基础与前沿研究计划(cstc2013jcyjA40005),重庆市研究生科研创新项目(CYB15052)
摘    要:针对传统高光谱影像地物分类算法大多仅考虑光谱信息而忽略空间邻近像元间相关性的问题,提出了一种空-谱协同嵌入(SSCE)降维算法和空-谱协同最近邻(SSCNN)分类器。首先,定义一种空-谱协同距离,并将其应用于近邻选取和低维嵌入;然后,构建空-谱近邻关系图来保持数据中的流形结构,并在权值设置中增大空间近邻点的权重以增强数据间的聚集性,提取鉴别特征;最后使用SSCNN分类器对降维后的数据进行分类。利用PaviaU和Salinas高光谱数据集进行试验验证,结果表明,与传统的光谱分类算法相比,该算法能有效提高高光谱影像的地物分类精度。

关 键 词:高光谱影像  维数简约  空-谱协同  流形结构  分类  
收稿时间:2016-01-01
修稿时间:2016-04-25

Hyperspectral Image Land Cover Classification Algorithm Based on Spatial-spectral Coordination Embedding
HUANG Hong,ZHENG Xinlei. Hyperspectral Image Land Cover Classification Algorithm Based on Spatial-spectral Coordination Embedding[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(8): 964-972. DOI: 10.11947/j.AGCS.2016.20150654
Authors:HUANG Hong  ZHENG Xinlei
Affiliation:Key Laboratory of Optoelectronic Technique and System of Ministry of Education, Chongqing University, Chongqing 400044, China
Abstract:Aiming at the problem that in hyperspectral image land cover classification,the traditional classification methods just apply the spectral information while they ignore the relationship between the spatial neighbors,a new dimensionality algorithm called spatial-spectral coordination embedding (SSCE) and a new classifier called spatial-spectral coordination nearest neighbor (SSCNN)were proposed in this paper.Firstly,the proposed method defines a spatial-spectral coordination distance and the distance is applied to the neighbor selection and low-dimensional embedding.Then,it constructs a spatial-spectral neighborhood graph to maintain the manifold structure of the data set,and enhances the aggregation of data through raising weight of the spatial neighbor points to extract the discriminant features.Finally,it uses the SSCNN to classify the reduced dimensional data.Experimental results using PaviaU and Salinas data set show that the proposed method can effectively improve ground objects classification accuracy comparing with traditional spectral classification methods.
Keywords:hyperspectral image  dimensionality reduction  spatial-spectral coordination  manifold structure  classification
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《测绘学报》浏览原始摘要信息
点击此处可从《测绘学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号