光谱与空间维双重稀疏表达的高光谱影像分类

doi:10.3724/SP.J.1047.2016.00263

摘要/Abstract

摘要：

高光谱遥感影像的稀疏分类是当前遥感信息处理的研究热点。本文提出一种光谱与空间双重稀疏表达的高光谱遥感影像分类方法（WSSRC）。首先利用小波字典对光谱维进行稀疏表示,将光谱维稀疏分类转化到小波域稀疏分类;其次,考虑空间邻域地物光谱的统一性和差异性,对邻域内像元分别进行稀疏编码,并对编码进行累加聚合;然后,利用聚合后的稀疏编码构造线性分类器对高光谱影像进行分类;最后,通过2幅标准的高光谱影像数据验证了本文所提出的方法。实验结果表明,该方法能有效地提高影像的分类精度。

关键词: 小波, 双重稀疏分类, 稀疏编码, 高光谱影像

Abstract:

A novel sparse representation classification model with spectral-spatial sparsity properties is presented to improve the classification accuracy of hyperspectral images. Firstly, this method uses the wavelet dictionary as the core dictionary to extract spectral domain sparse information, and then the spectral dimension sparse representation classification is transformed into the wavelet domain (WSRC) by inverse wavelet transformation. After that, we actually extract the sparse spectral features of the hyperspectral images and increase the recognition of the original dictionary. Secondly, considering the unity and diversity of the spatial adjacent object, we realize the sparse coding of the neighborhood pixels, and then accumulate the sparse codes. At the same time, we classify the hyperspectral images using a linear classifier that is based on the accumulated sparse codes. This method ensures that we extract the main sparse signal of the neighborhood pixels on the basis of the personality features of sparse encoding, and it performs better than the joint sparse representation model (JSRC) which is directly based on the neighborhood pixels. Finally, two commonly used hyperspectral images are utilized to validate the proposed model. The experimental results demonstrate that the proposed algorithm outperforms other models in terms of overall accuracy and kappa coefficient measurements.

Key words: wavelet transform, double sparse representation, sparse code, hyperspectral image

朱勇, 吴波. 光谱与空间维双重稀疏表达的高光谱影像分类[J]. 地球信息科学学报, 2016, 18(2): 263-271.DOI:10.3724/SP.J.1047.2016.00263

ZHU Yong,WU Bo. Classification of Hyperspectral Images with Spectral-Spatial Sparse Representation[J]. Journal of Geo-information Science, 2016, 18(2): 263-271.DOI:10.3724/SP.J.1047.2016.00263

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参考文献 24

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序号	类别	训练	测试
1	苜蓿	5	41
2	玉米I	143	1285
3	玉米II	83	747
4	玉米III	24	213
5	草I	49	343
6	草II	73	657
7	草III	3	25
8	干草梗	48	430
9	燕麦	2	18
10	大豆I	98	874
11	大豆II	246	2209
12	大豆III	60	533
13	小麦	21	184
14	树丛	127	1138
15	建筑物-草-树-路	39	347
16	石-钢顶棚	10	83
总计		1031	9218

小波基层次	DB4			Sym2		Coif 2			Dmey
小波基层次	OA		St		OA	St	OA	St	OA	St
原始层	70.10	0.98		70.10	0.98	70.10	0.98		70.10	0.98
第1层	72.70	1.01		73.29	1.02	72.77	1.01		73.60	1.01
第2层	74.00	1.03		73.71	1.03	74.32	1.03		75.40	1.03
第3层	65.94	1.04		62.95	1.03	67.78	1.03		69.28	1.02

Dmey	类内距离	类间距离	信息损失量	结构指标St
原始层	0.0031	0.0129	0.0000	0.0098
第1层	0.0027	0.0128	0.0022	0.0101
第2层	0.0024	0.0127	0.0039	0.0103
第3层	0.0022	0.0123	0.0102	0.0102
第4层	0.0019	0.0109	0.0305	0.0090

类别	SRC	WSRC	JSRC	WSSRC
苜蓿	39.02	75.61	21.95	92.68
玉米I	60.31	65.45	94.09	96.11
玉米II	52.61	59.30	84.20	91.83
玉米III	32.39	42.25	85.92	95.31
草I	86.64	89.63	94.24	94.70
草II	95.74	95.89	99.70	99.70
草III	64.00	84.00	80.00	96.00
干草梗	95.81	97.44	100.00	100.00
燕麦	55.56	61.11	0.00	0.00
大豆I	56.86	69.11	85.47	91.42
大豆II	69.58	73.38	94.43	98.37
大豆III	49.91	60.41	95.87	97.94
小麦	98.37	98.37	100.00	99.46
树丛	89.89	89.10	98.51	98.86
建筑物-草-树-路	41.21	51.87	85.59	93.95
石-钢顶棚	79.52	80.72	97.59	98.80
OA	69.53	74.46	92.98	96.46
kappa系数	0.651	0.708	0.920	0.960

序号	类别	训练	测试
1	柏油	664	5967
2	草甸	1865	16 784
3	碎石	210	1889
4	树	307	2757
5	金属板	135	1210
6	裸土	503	4526
7	沥青	133	1197
8	砖	369	3313
9	阴影	95	852
总计		4281	38 495