Super-resolution reconstruction algorithm based on relevance vector machine for hyperspectral image

被引：0

作者：

Wang, Xiaofei ^{[1
,2
]}

Yan, Qiujing ^{[2
]}

Zhang, Junping ^{[3
]}

Wang, Aihua ^{[1
,4
]}

机构：

[1] Beijing Twenty-First Century Science and Technology Development Co. Ltd, Beijing

[2] Key Laboratory of Senior-Education for Electronic Engineering, Heilongjiang University, Harbin, 150080 , Heilongjiang

[3] Department of Information Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang

[4] Twenty First Century Aerospace Technology Co. Ltd, Beijing

来源：

Zhongguo Jiguang/Chinese Journal of Lasers | 2014年 / 41卷

关键词：

Information fusion; Relevance vector machine; Remote sensing hyperspectral; Super-resolution;

D O I：

10.3788/CJL201441.s114001

中图分类号：

学科分类号：

摘要：

In order to improve the space resolution of hyper-spectral image by fusing the spatial information of multispectral images and the spectral information of hyperspectral images, a hyperspectral image super-resolution algorithm based on relevance vector machine (RVM) is proposed. A brief introduction of the principle of the Price method which fuses multispectral and hyperspectral images to get the super-resolution image is given, and the RVM linear regression is introduced. Combining with the advantages of RVM in regression analysis, a resolution enhancement by revealing the corrspondence of the spatial and spectral information is gotten. The experiment results show that the normalized root-mean-square (RMS) is lower than 0.001 and the spectral angel error is lower than 0.02, which gets a great improvement compared with the results of the Price method and the Elbakary method. The method proposed has a significant result in hyperspectral image reconstruction, which provides a much properer data source for classification, object detection and recognition.

引用

页数：5

共 10 条

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