基于端元字典稀疏解混的高光谱图像亚像元定位

doi:10.16180/j.cnki.issn1007-7820.2019.04.008

电子科技 ›› 2019, Vol. 32 ›› Issue (4): 33-39.doi: 10.16180/j.cnki.issn1007-7820.2019.04.008

基于端元字典稀疏解混的高光谱图像亚像元定位

顾正之^1,^2,³,王素玉^1,^2,³

^1. 北京未来网络科技创新中心,北京 100124
^2. 北京物联网软件与系统工程研究中心,北京 100124
^3. 北京工业大学信息学部,北京 100124

收稿日期:2018-03-18 出版日期:2019-04-15 发布日期:2019-03-27
作者简介:顾正之(1991-),男,硕士研究生。研究方向:数字图像处理。
基金资助:
国家自然科学基金(61201361);北京市教委科学基金(KM201710005011);北京市人才培养计划基金(2013D005015000008)

Hyperspectral Image Sub-Pixel Mapping Based on Sparse Unmixing of Endmember Dictionary

GU Zhengzhi^1,^2,³,WANG Suyu^1,^2,³

^1. Beijing Advanced Innovation Center for Future Internet Technology, Beijing 100124,China
^2. Beijing Engineering Research Center for IoT Software and Systems, Beijing 100124,China
^3. Faculty of Information Technology, Beijing University of Technology, Beijing 100124,China

Received:2018-03-18 Online:2019-04-15 Published:2019-03-27
Supported by:
National Natural Science Foundation of China(61201361);Science Foundation of the Beijing Education Commission(KM201710005011);Training Program Foundation for the Talents in Beijing City(2013D005015000008)

摘要/Abstract

摘要：

针对高光谱图像中普遍存在的混合像元中各端元空间分布定位困难的问题,文中提出一种基于K-SVD的光谱解混算法,利用其解混结果进行亚像元定位。算法首先通过KNN分类来区分待处理图像中的混合像元和纯像元,然后借鉴基于冗余字典的稀疏分解相关理论,以标准光谱库为基础,通过基于K-SVD的字典训练算法训练产生最具代表性的地物光谱曲线,构建端元冗余字典,通过基于K-SVD的稀疏分解算法实现各端元丰度的求解。最后利用求得的丰度系数在两种空间性相关性约束下进行亚像元定位。实验结果表明,采用该算法进行模拟数据和真实数据的亚像元的定位可以取得不错的定位结果。

关键词: 高光谱图像, 光谱解混, 亚像元定位, K-SVD, 稀疏表示, 冗余字典

Abstract:

In general, it is difficult to locate the spatial distribution of each endmember in the hyperspectral image. To solve the problem, this study proposed a K-SVD-based spectral unmixing algorithm whose result of the demixing was further used for performing the subpixel location. First, the mixed pixel and pure pixel were distinguished by KNN classification, and then the sparse decomposition correlation theory based on redundant dictionary was used for reference. Based on the standard spectral library, the K-SVD based dictionary training algorithm was used to train the most representative material spectral curves, and the endmember redundancy dictionary was subsequently constructed. The abundances were solved by the sparse decomposition algorithm based on K-SVD. Finally, the obtained abundance coefficient was used to locate the sub-pixel under the two spatial correlation constraints. Experimental results showed that the proposed algorithm had reliable performance for the sub-pixel mapping effects of simulated data and measured data.

Key words: hyperspectral image, spectral unmixing, sub-pixel mapping, K-SVD, sparse representation, redundant dictionary

中图分类号:

TP391

顾正之,王素玉. 基于端元字典稀疏解混的高光谱图像亚像元定位[J]. 电子科技, 2019, 32(4): 33-39.

GU Zhengzhi,WANG Suyu. Hyperspectral Image Sub-Pixel Mapping Based on Sparse Unmixing of Endmember Dictionary[J]. Electronic Science and Technology, 2019, 32(4): 33-39.

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	NCLS ^[9]	SUnSAL ^[12]	CLSUnSAL ^[21]	GSUnSAL ^[13]	本文算法
20DB	1.051 4	0.384 7	0.273 5	0.272 0	0.237 7
30DB	0.486 5	0.264 6	0.230 6	0.229 2	0.197 5
40DB	0.281 7	0.148 1	0.118 0	0.101 9	0.097 9

	NCLS	SUnSAL	CLSUnSAL	GSUnSAL	Proposed
20DB	-5.864 9	2.867 4	5.830 9	5.826 4	6.143 2
30DB	0.828 5	6.118 7	7.312 7	7.388 9	7.511 2
40DB	5.573 0	11.153 6	13.130 6	14.409 2	14.382 9

	对比算法^[18]	提出的算法
块状数据	0.015 8	0.010 6
小目标数据	0.026 2	0.024 8

基于端元字典稀疏解混的高光谱图像亚像元定位

Hyperspectral Image Sub-Pixel Mapping Based on Sparse Unmixing of Endmember Dictionary

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