基于二级字典的联合稀疏表示高光谱图像分类

doi:10.3969/j.issn.1001-506X.2020.03.007

Abstract

Abstract:

In view of the problems of low classification accuracy and insufficient utilization of spectral information in most traditional classification algorithms when applied in hyperspectral classification, based on the joint sparse representation of hyperspectral image classification based on kernel function, the joint sparse representation of hyperspectral image classification based on secondary dictionary is proposed. The gravitation between the pixel to be measured and the atom is added in front of the dictionary atom, so as to find the atom matching with the pixel to be measured more quickly. The added gravitational value is calculated by the formula adapted to the hyperspectral image modified by the gravitational formula. In order to explore the meaningful classification and identification information contained in the residual band after sparse reconstruction fully, this paper reuses the residual information is reused by using exponential smoothing formula. Experimental results in Indian Pines and Salina-A data sets show that the proposed algorithm achieves the purpose of improving the classification accuracy of hyperspectral images.

Key words: hyperspectral image classification, joint sparse representation, gravitation formula, secondary dictionary, adaptive

CLC Number:

TP751

Shanxue CHEN, Wenwen CHEN. Joint sparse representation of hyperspectral image classification based on secondary dictionary[J]. Systems Engineering and Electronics, 2020, 42(3): 550-556.

Figures/Tables 12

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References 21

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算法	字典原子个数/个
算法	5	30	50	70	100
SD-JSRC(本文)	67.81	83.24	87.96	91.37	95.98
K-JSRC(CEK)	65.03	77.93	83.85	86.89	89.46
KNN	56.26	73.31	76.15	77.97	82.59
cdSRC	59.80	76.18	80.80	83.86	86.71
OMP	58.97	74.43	77.52	80.44	83.77

算法	时间/s
SD-JSRC	816.47
K-JSRC	788.63
KNN	25.38
cdSRC	517.18
OMP	6.51

算法	字典原子个数/个
算法	10	30	100	150	200
SD-JSRC(本文)	85.07	93.62	97.05	98.50	99.18
K-JSRC(CEK)	84.39	92.83	95.95	96.37	97.20
KNN	77.85	83.57	85.60	87.10	88.14
cdSRC	81.38	89.05	93.42	94.13	94.72
OMP	76.92	82.75	85.95	87.73	88.92

类别	字典原子个数/个	测试样本个数/个	OMP	cdSRC	KNN	K-JSRC	改进算法
1	150	2 009	98.66	100.00	98.01	100.00	100.00
2	150	3 726	98.63	99.92	98.95	99.70	99.92
3	150	1 976	96.46	100.00	98.94	100.00	100.00
4	150	1 394	98.92	99.86	99.28	98.92	99.57
5	150	2 678	95.37	99.03	95.59	98.84	98.95
6	150	3 959	99.75	99.80	99.22	99.72	99.75
7	150	3 579	99.72	99.80	99.16	99.80	99.92
8	150	11 271	69.05	90.87	63.22	92.17	99.65
9	150	6 023	98.61	98.68	96.66	100.00	100.00
10	150	3 278	91.85	99.60	90.51	97.44	98.72
11	150	1 068	98.78	99.72	94.57	99.16	99.63
12	150	1 927	96.32	100.00	99.90	99.95	100.00
13	150	916	95.74	98.91	97.05	99.02	98.80
14	150	1 070	96.54	95.79	93.55	96.54	96.54
15	150	7 268	62.16	74.61	69.36	88.43	92.45
16	150	1 807	99.06	99.00	98.62	99.67	99.89
总体分类精度			86.93	94.29	86.40	96.41	98.64

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Joint sparse representation of hyperspectral image classification based on secondary dictionary

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