An Informative Feature Selection Method Based on Sparse PCA for VHR Scene Classification

IEEE Geoscience and Remote Sensing Letters

Volumn 13, Issue 2, 2016, Pages 147-151

  Chaib, Souleyman ^a   Gu, Yanfeng ^a   Yao, Hongxun ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Feature selection; scene classification; sparse principal component analysis (sPCA)

Indexed keywords

FEATURE EXTRACTION; LAND USE; PRINCIPAL COMPONENT ANALYSIS; SATELLITE IMAGERY;

FEATURE SELECTION METHODS; SCALE INVARIANT FEATURE TRANSFORMS; SCENE CLASSIFICATION; SPARSE PRINCIPAL COMPONENT ANALYSIS; SPEEDED UP ROBUST FEATURES; STATE-OF-THE-ART METHODS; UNIVERSITY OF CALIFORNIA; VERY HIGH RESOLUTION;

CLASSIFICATION (OF INFORMATION);

EID: 84953306070     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2015.2501383     Document Type: Article

References (19)

1. J. Sivic and A. Zisserman, "Video Google: A text retrieval approach to object matching in videos," in Proc. 9th IEEE Int. Conf. Comput. Vis., Oct. 13-16, 2003, vol. 2, pp. 1470-1477.
2. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 2006, vol. 2, pp. 2169-2178.
3. L. Zhao, P. Tang, and L. Huo, "Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 12, pp. 4620-4631, Dec. 2014.
4. C. Vaduva, I. Gavat, and M. Datcu, "Latent Dirichlet allocation for spatial analysis of satellite images," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 5, pp. 2770-2786, May 2013.
5. M. Blei, Y. Ng, and I. Jordan, "Latent Dirichlet allocation," J. Mach. Learn. Res., vol. 3, pp. 993-1022, Jan. 2003.
6. A. M. Cheriyadat, "Unsupervised feature learning for aerial scene classification," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 1, pp. 439-451, Jan. 2014.
7. F. Zhan, B. Du, and L. Zhang, "Saliency-guided unsupervised feature learning for scene classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 4, pp. 2176-2184, Apr. 2015.
8. G. Cheng, J. Han, P. Zhou, and L. Guo, "Multi-class geospatial object detection and geographic image classification based on collection of part detectors," ISPRS J. Photogramm. Remote Sens., vol. 98, pp. 119-132, Dec. 2014.
9. G. Cheng et al., "Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 8, pp. 4238-4249, Aug. 2015.
10. P. Turcot and D. Lowe, "Better matching with fewer features: The selection of useful features in large database recognition problems," in Proc. IEEE ICCV Workshop, 2009, pp. 2109-2116.
11. D. Lowe, "Object recognition from local scale-invariant features," in Proc. IEEE ICCV, 1999, vol. 2, pp. 1150-1157.
12. H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "SURF: Speeded up robust features," Comput. Vis. Image Understand., vol. 110, no. 3, pp. 346-359, Jun. 2008.
13. H. Zou, T. Hastie, and R. Tibshirani, "Sparse principal component analysis," J. Comput. Graph. Stat., vol. 15, no. 2, pp. 265-286, Jun. 2006.
14. N. Naikal, A. Y. Yang, and S. S. Sastry, "Informative feature selection for object recognition via sparse PCA," in Proc. IEEE ICCV, 2011, pp. 818-825.
15. A. d'Aspremont, L. El Ghaoui, M. Jordan, and G. Lanckriet, "A direct formulation for sparse PCA using semidefinite programming," SIAM Rev., vol. 49, no. 3, pp. 434-448, Jul. 2007.
16. Y. Yang and S. Newsam, "Bag-of-visual-words and spatial extensions for land-use classification," in Proc. ACM Int. Conf. Adv. Geograph. Inf. Syst., 2010, pp. 270-279.
17. B. Du and L. P. Zhang, "Target detection based on a dynamic subspace," Pattern Recognit., vol. 47, no. 1, pp. 344-358, Jan. 2014.
18. Y. Yang and S. Newsam, "Spatial pyramid co-occurrence for image classification," in Proc. IEEE ICCV, Nov. 6-13, 2011, pp. 1465-1472.
19. L. Mekhalfi, F. Melgani, Y. Bazi, and N. Alajlan, "Land-use classification with compressive sensing multifeature fusion," IEEE Trans. Geosci. Remote Sens., vol. 12, no. 10, pp. 2155-2159, Oct. 2015.