Research on the Optimal Classification Method for Remote Sensing Image Based on the Gabor-PCA Analysis

Abstract:

According to the problem that the traditional remote sensing image classification methods focus only on analyzing the spectral features and have low utilization of the spatial information, a new spatial-spectral classification method is proposed in this paper, its core idea is to combine the spectral features base on the Principal Component Analysis (PCA) algorithm with the spatial features extracted by the Gabor filter. Experiments show that, compared with the traditional classification methods, the proposed method can improve the classification accuracy and the Kappa coefficient, which means to bring better classification and visual effects.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 3617-3620
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.3617
• Online since: July 2014
• Authors: Jing Hui Yang*, Li Guo Wang, Jin Xi Qian
• Keywords: Classification, PCA, Remote Sensing Image, Spatial-Spectra Features

References

[1] Rosa Maria Cavalli, Giorgio Antonino Licciardi, Jocelyn Chanussot, Archaeological Structures Using Nonlinear Principal Component Analysis Applied to Airborne Hyperspectral Image [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013 6(2) 659-669.

DOI: 10.1109/jstars.2012.2227301

[2] Wang Liguo, Liu Danfeng, Wang Qunming. Spectral Unmixing Model Based on Least Squares Support Vector Machine with Unmixing Residue Constraints [J]. IEEE Geoscience and Remote Sensing Letters . 2013 10(6) 1592-1596.

DOI: 10.1109/lgrs.2013.2262371

[3] Anil K. Jain, Farshid Farrokhnia, Unsupervised texture segmentation using Gabor filters [J]. Pattern Recognition, 1991 24(12) 1167-1186.

DOI: 10.1016/0031-3203(91)90143-s

[4] Chuan-Xian Ren, Dao-Qing Dai, Xiao-Xin Li, Band-Reweighed Gabor Kernel Embedding for Face Image Representation and Recognition [J]. IEEE Transactions on Image Processing, 2014 23(2) 725-740.

DOI: 10.1109/tip.2013.2292560

[5] Linlin Shen, Zexuan Zhu, Sen Jia, Discriminative Gabor Feature Selection for Hyperspectral Image Classification [J]. IEEE Geoscience and Remote Sensing Letters, 2013 10(1) 29-33.

DOI: 10.1109/lgrs.2012.2191761

[6] Antonio Plaza, Jon Atli Benediktsson, Joseph W. Boardman, Recent advances in techniques for hyperspectral image processing [J]. Remote Sensing of Environment, 2009 113 s110-s122.

DOI: 10.1016/j.rse.2007.07.028

[7] Guangyi Chen, Shen-En Qian, Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011 49(3) 973-980.

DOI: 10.1109/tgrs.2010.2075937

[8] Qian Du, James E. Fowler, Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis [J]. IEEE Transactions on Geoscience and Remote Sensing, 2007 4 (2) 201-205.

DOI: 10.1109/lgrs.2006.888109

[9] John Wright, Allen Y. Yang, Arvind Ganesh, Robust Face Recognition via Sparse Representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009 31(2) 210-227.

DOI: 10.1109/tpami.2008.79

[10] Yi Chen, Nasser M. Nasrabadi, Trac D. Tran, Sparse Representation for Target Detection in Hyperspectral Imagery [J]. IEEE Journal of Selected Topics in Signal Processing, 2011 5(3) 629-640.

DOI: 10.1109/jstsp.2011.2113170

[11] Benqin Song, Jun Li, Mauro Dalla Mura, Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles [J]. IEEE Transactions on Geoscience and Remote Sensing, 2014 52(8) 5122-5136.

DOI: 10.1109/tgrs.2013.2286953

[12] Yanfeng Gu, Kai Feng. Optimized Laplacian SVM With Distance Metric Learning for Hyperspectral Image Classification [J]. Selected Topics in Applied Earth Observations and Remote Sensing, 2013 6(3) 1109-1117.

DOI: 10.1109/jstars.2013.2243112