Hyperspectral Image Classification via Discriminative Sparse Representation with Extended LBP Texture

Yue Mei Ren; Yan Ning Zhang; Wei Wei

doi:10.4028/www.scientific.net/AMR.989-994.3885

Paper Titles

The Simulation of DS-UWB RAKE Receiver over the Multipath Channel
p.3868

Video Sequences Foreground Enhancement Using Hidden Markov Model
p.3872

The Study of a Fast Sub-Pixel Registration Method for Remote Sensing Image
p.3877

The Study of Combination Filtering for Removing Mixed Noise of Remote Sensing Image
p.3881

Hyperspectral Image Classification via Discriminative Sparse Representation with Extended LBP Texture
p.3885

Channel Reservation Strategies for Multiple Secondary Users in Cognitive Radio Networks
p.3889

New Enhancement Algorithm of Infrared Images Based on Dual Threshold of the Exponential Transform
p.3893

Novel Single Image Dehazing Algorithm
p.3898

Simulation and Application of Space-Time Coding in OFDM System in Coal Mines
p.3902

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 989-994Hyperspectral Image Classification via...

Hyperspectral Image Classification via Discriminative Sparse Representation with Extended LBP Texture

Abstract:

Hyperspectral images (HSI) have rich texture information, so combining texture information and image spectral information can improve the recognition accuracy. Sparse representation has significant success in image classification. In this paper, we propose a new discriminative sparse-based classification framework using spectral data and extended Local Binary Patterns (LBP) texture. Firstly, we propose an extended LBP coding for HSI classification. Then we formulate an optimization problem that combines the objective function of classification with the representation error by sparsity. Furthermore, we use a procedure similar to K-SVD algorithm to learn the discriminative dictionary. The experimental results show that the proposed discriminative spasity-based classification of image including the extended LBP texture outperforms the classical HSI classification algorithms.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

3885-3888

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.3885

Citation:

Cite this paper

Online since:

July 2014

Authors:

Yue Mei Ren*, Yan Ning Zhang, Wei Wei

Keywords:

Dictionary Learning, Hyperspectral Image Classification, Local Binary Pattern (LBP), Sparse Representation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] M. Borengasser, W. S. Hungate, and R. Watkins: Boca Raton, FL: CRC Press, (2008).

Google Scholar

[2] H. Chavez-Roman, V. Ponomaryov: IEEE Geoscience and Remote Sensing Letters, vol. PP, no. 99, pp.1-5(2014).

Google Scholar

[3] Weisheng Dong, Lei Zhang, Guangming Shi, Xin Li: IEEE Transactions on Image Processing, vol. 22, no. 4, pp.1620-1630( 2013).

Google Scholar

[4] John Wright, Allen Yang, Arvind Ganesh, Shankar Satry, and Yi Ma: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp.210-217 (2009).

Google Scholar

[5] Ehsan Elhamifar Ren´ e Vidal: 2011 IEEE Conference on Computer Vision and Pattern Recognition. pp.1873-1880(2011).

Google Scholar

[6] H. Zhang, J. Li, Y. Huang, L. Zhang: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. vol. PP, no. 99, pp: 1-10(2013).

Google Scholar

[7] Li P J, Cheng T, Guo J C: Photo-grammetric Engineering and Remote Sensing. Vol. 75, no. 2, pp.147-157(2009).

Google Scholar

[8] T. Ojala, M. Pietikainen, and D. Harwood: Pattern Recognition, vol. 29, no. 1, pp.51-59(1996).

Google Scholar