| 自动子空间划分在高光谱影像波段选择中的应用 |
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| 引用本文： | 苏红军,盛业华,杜培军.自动子空间划分在高光谱影像波段选择中的应用[J].地球信息科学,2007,9(4):123-128. |
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| 作者姓名： | 苏红军 盛业华 杜培军 |
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| 作者单位： | 1. 南京师范大学虚拟地理环境教育部重点实验室, 南京 210046;
2. 中国矿业大学地理信息与遥感科学系, 徐州 221008 |
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| 基金项目： | 国家自然科学基金项目(40401038)，地理空间信息工程国家测绘局重点实验室开放基金项目，中国矿业大学科学基金项目(D200403)。 |
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| 摘 要： | 针对高光谱遥感影像数据量大、维数高的特点,结合联合熵波段选择算法,提出了一种自动子空间划分的改进方案。该方法充分利用了影像各波段数据之间的局部相关性,根据波段间相关系数矩阵图像的"分块"特点,将整个波段空间自动划分为若干个子空间,然后再进行波段选择。实现了在删减冗余信息的同时选择出含有主要信息的特征波段组合的目的。将此方法得到的结果与用联合熵得到的结果进行了比较分析,结果表明自动子空间划分的联合熵波段选择方法具有较好的效果。
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| 关 键 词： | 高光谱遥感 波段选择 自动子空间划分 联合熵 |
| 收稿时间： | 2006-09-07; |
| 修稿时间： | 2006-09-07 |
Study on Auto-Subspace Partition for Band Selection of Hyperspectral Image |
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| SU Hongjun,SHENG Yehua,DU Peijun.Study on Auto-Subspace Partition for Band Selection of Hyperspectral Image[J].Geo-information Science,2007,9(4):123-128. |
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| Authors: | SU Hongjun SHENG Yehua DU Peijun |
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| Institution: | 1. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210046, China;
2. Department of Geographic Information and Remote Sensing Science, China University of Mining and Technology, Xuzhou 221008, China |
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| Abstract: | Recent works on spectral band selection include two separate tasks: feature band selection and redundancy reduction. But due to the characteristic of hyperspectral data,it is not sufficient for joint entropy algorithm to select feature bands which aim at dimensionality reduction,for the band combination results it selected are in a series of space. To solve this problem,a new approach based on auto-subspace partition (ASP) was proposed. In this approach the subspace of all bands was dependent on correlation coefficient matrix among all bands,and from that we can get the relations among different bands about its spectral characteristic. In ASP,firstly,all bands were divided into different subspaces according to correlation coefficient matrix,then the optimal bands combination was selected using joint entropy algorithm respectively in different subspaces. The band combination results which were derived from our proposed approach were compared with those from joint entropy algorithm in the experiments. It has shown that the approach we proposed works better than the conventional joint entropy algorithms on hyperspectral data. |
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| Keywords: | hyperspectral RS band selection auto-subspace partition joint entropy |
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