| 利用可分离非负矩阵分解实现高光谱波段选择 |
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| 引用本文： | 杨刚,孙伟伟,张殿发.利用可分离非负矩阵分解实现高光谱波段选择[J].武汉大学学报(信息科学版),2019,44(5):737-744. |
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| 作者姓名： | 杨刚 孙伟伟 张殿发 |
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| 作者单位： | 1.宁波大学地理与空间信息技术系, 浙江 宁波, 315211 |
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| 基金项目： | 国家自然科学基金41671342国家自然科学基金41801256浙江省自然科学基金LR19D010001浙江省自然科学基金LQ18D010001地球观测与时空信息科学国家测绘地理信息局重点实验室开放基金201804测绘遥感信息工程国家重点实验室开放基金18R05 |
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| 摘 要： | 高光谱影像波段众多且相关性强，导致分类存在信息冗余且计算量较大。提出了可分离非负矩阵分解方法来选取高光谱影像的代表性波段子集，在保证分类精度的同时降低计算量。该方法假设高光谱影像的波段集合具有可分离特性，改进传统非负矩阵分解模型，将波段选择转换为可分离非负矩阵分解问题，采用迭代投影方法来依次选取能够非负线性表达其他波段的代表性波段。在此基础上，利用两个公开高光谱数据集对比几种主流方法，采用定量评价和分类精度指标来综合评价所提的波段选择方法的效果。实验结果表明，可分离非负矩阵分解方法的分类精度高于其他几种方法，而且计算效率排名第2，能够选取合适的波段子集以满足高光谱遥感的应用需求。
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| 关 键 词： | 高光谱影像 可分离非负矩阵分解 波段选择 迭代投影 |
| 收稿时间： | 2018-04-17 |
Separable Nonnegative Matrix Factorization Based Band Selection for Hyperspectral Imagery |
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| Affiliation: | 1.Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China |
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| Abstract: | Strong intra-band correlations along with numerous bands seriously hinder the processing and applications of hyperspectral remote sensing images in realistic applications. A separable non-negative matrix factorization (SepNMF) method is presented to explore the band selection problem on hyperspectral imagery (HSI). The method investigates the separability structure in the band set of the HSI data to improve the regular non-negative matrix factorization model, and it formulates the band selection problem into the problem of finding representative columns that represent other bands with non-negative and linear combinations in the SepNMF model. The method adopts the recursive projection method to iteratively select the representative bands to constitute the proper band subset. Three groups of experiments on two open HSI data sets are designed to carefully testify the performance of the SepNMF in band selection. Several popular methods are utilized to compare against the proposed SepNMF method. Experimental results show that the SepNMF obtains the best overall classification accuracies of all while taking shorter computational times ranking second among all the comparison methods. Therefore, the SepNMF method can be an alternative choice for selecting proper bands in hyperspectral image classification. |
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