Hyperspectral image classification based on joint structured sparse representation

被引：0

作者：

Bo, Chunjuan ^{[1
]}

Zhang, Rubo ^{[1
]}

Yang, Dawei ^{[1
]}

Gong, Tao ^{[1
]}

机构：

[1] College of Electromechanical and Information Engineering, Dalian Nationalities University, Dalian, 116600, Liaoning

来源：

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2015年 / 43卷

关键词：

Hyperspectral image; Image processing; Joint sparse representation; Sparse representation; Structured sparse representation;

D O I：

10.13245/j.hust.15S1045

中图分类号：

学科分类号：

摘要：

In order to achieve accurate hyperspectral image (HSI) classification, with combining sparse representation and the spectral information, a novel HSI classification algorithm was proposed based on joint structured sparse representation. This algorithm is able to exploit the spatial contextual information of the testing pixels and the structure information among dictionaries in the meanwhile. A joint sparse structured sparse representation model was built, and an effective solution method was developed by using the alternating direction method of multipliers (ADMM) method. Based on the proposed model, a HSI classification framework was designed based on joint structured sparse representation, in which the class-specific residue manner was adopted to determine the class of testing pixels. The experimental results demonstrate that the proposed method can achieve better accurate classification performance than other classical or state-of-the-arts algorithms. ©, 2015, Huazhong University of Science and Technology. All right reserved.

引用

页码：187 / 191

页数：4

共 15 条

[1]

Camps-Valls G., Gomez-Chova L., Muoz-Mar J., Et al., Composite kernels for hyperspectral image classification, IEEE Geoscience and Remote Sensing Letters, 3, 1, pp. 93-97, (2006)

[2]

Li J., Marpu P.R., Plaza A., Et al., Generalized composite kernel framework for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, 51, 9, pp. 4816-4829, (2013)

[3]

Mura M.D., Benediktsson J.A., Waske B., Et al., Morphological attribute profiles for the analysis of very high resolution images, IEEE Transactions on Geoscience and Remote Sensing, 48, 10, pp. 3747-3762, (2010)

[4]

Krishnapuram B., Carin L., Figueiredo M.A.T., Et al., Sparse multinomial logistic regression: fast algorithms and generalization bounds, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 6, pp. 957-968, (2005)

[5]

Moser G., Serpico S.B., Combining support vector machines and markov random fields in an integrated framework for contextual image classification, IEEE Transactions on Geoscience and Remote Sensing, 51, 1-5, pp. 2734-2752, (2013)

[6]

Srinivas U., Chen Y., Monga V., Et al., Exploiting sparsity in hyperspectral image classification via graphical models, IEEE Geoscience and Remote Sensing Letters, 10, 3, pp. 505-509, (2013)

[7]

Liu J., Wu Z., Sun L., Et al., Hyperspectral image classification using kernel sparse representation and semilocal spatial graph regularization, IEEE Geoscience and Remote Sensing Letters, 11, 8, pp. 1320-1324, (2014)

[8]

Chen Y., Nasrabadi N.M., Tran T.D., Hyperspectral image classification using dictionary-based sparse representation, IEEE Transactions on Geoscience and Remote Sensing, 49, 10, pp. 3973-3985, (2011)

[9]

Chen Y., Nasrabadi N.M., Tran T.D., Hyperspectral image classification via kernel sparse representation, IEEE Transactions on Geoscience and Remote Sensing, 51, 1, pp. 217-231, (2013)

[10]

Li J., Zhang H., Huang Y., Et al., Hyperspectral image classification by nonlocal joint collaborative representation with a locally adaptive dictionary, IEEE Transactions on Geoscience and Remote Sensing, 52, 6, pp. 3707-3719, (2014)

← 1 2 →