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

Abstract

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

CLC Number:

TP391

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.

Figures/Tables 12

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References 18

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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

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

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