高光谱影像稀疏解混的空间同质分析法

测绘学报 ›› 2014, Vol. 43 ›› Issue (6): 607-612.

高光谱影像稀疏解混的空间同质分析法

王毓乾,邵振峰

武汉大学测绘遥感信息工程国家重点实验室

收稿日期:2013-12-13 修回日期:2014-03-11 出版日期:2014-06-25 发布日期:2014-06-25
通讯作者: 王毓乾 E-mail:neo@whu.edu.cn
基金资助:
国家自然科学基金;国家自然科学基金;国家973计划;国家863计划;国家科技专项;国家科技专项;中央高校基本科研业务费专项资金

Sparse Unmixing for Hyperspectral Image Based on Spatial Homogeneous Analysis

Received:2013-12-13 Revised:2014-03-11 Online:2014-06-25 Published:2014-06-25

摘要/Abstract

摘要：

本文针对高光谱遥感影像端元丰度的稀疏性和空间分布平滑性，提出一种基于空间同质分析的稀疏解混算法。该算法首先对高光谱影像进行空间同质分析来提取同质指数，然后根据同质指数对稀疏回归解混模型中的空间正则项赋予不同权重，使其能更好地反映高光谱影像端元丰度分布的空间复杂性，进而实现对高光谱混合像元的有效分解。模拟数据和真实数据的实验分析表明：本文提出的算法能更好地保持结果的稀疏性和丰度空间分布的平滑性并且具有一定的抗噪性，提高了整体的解混精度。

关键词: 高光谱影像, 光谱分解, 稀疏回归, 同质分析

Abstract:

Endmember abundance of hyperspectral imagery is of notable sparsity and distributing smoothness in spatial space. According to these two properties, a sparse unmixing algorithm based on imagery spatial homogeneity analysis is proposed in this paper. Firstly, homogeneity index is calculated by imagery spatial homogeneity analysis. Then the spatial regularizers of the sparse regression unmixing model are weighted according to the homogeneity index. This model can reflect the spatial distribution complexity of endmember abundance and make the unmixing process more effective. Experiments on both simulated and real hyperspectral data show that this algorithm well keeps unmixing abundance sparsity and spatially smoothness with good noise immunity and promotes entire unmixing accuracy.

Key words: hyperspectral imagery, spectral unmixing, sparse regression, homogeneity analysis

中图分类号:

P237

王毓乾邵振峰. 高光谱影像稀疏解混的空间同质分析法[J]. 测绘学报, 2014, 43(6): 607-612.

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