Dynamic classifier selection using spectral-spatial information for hyperspectral image classification

Journal of Applied Remote Sensing

Volumn 8, Issue 1, 2014, Pages

  Su, Hongjun ^a,b   Yong, Bin ^a   Du, Peijun ^b   Liu, Hao ^c   Chen, Chen ^d   Liu, Kui ^d  

^a HOHAI UNIVERSITY   (China)

^b NANJING UNIVERSITY   (China)

^c WUHAN UNIVERSITY   (China)

^d UNIVERSITY OF TEXAS AT DALLAS   (United States)

Author keywords

dynamic classifier selection; hyperspectral image classification; spectral feature; volumetric textural feature

Indexed keywords

COVARIANCE MATRIX; EXTRACTION; FEATURE EXTRACTION; HYPERSPECTRAL IMAGING; IMAGE CLASSIFICATION; IMAGE ENHANCEMENT; IMAGE REGISTRATION; INFORMATION USE; PIXELS; REMOTE SENSING; SPECTROSCOPY; TEXTURES;

CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; DYNAMIC CLASSIFIER SELECTION; GRAY LEVEL CO-OCCURRENCE MATRIX; HYPERSPECTRAL REMOTE SENSING; SPECTRAL FEATURE; SPECTRAL FEATURE EXTRACTION; TEXTURAL FEATURE;

CLASSIFICATION (OF INFORMATION);

EID: 84924118487     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.JRS.8.085095     Document Type: Article

References (37)

1. M. D. Mura et al., "Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis, " IEEE Geosci. Remote Sens. Lett. 8(3), 542-546 (2011).
2. P. Gamba et al., "Improved VHR urban area mapping exploiting object boundaries, " IEEE Trans. Geosci. Remote Sens. 45(8), 2676-2682 (2007).
3. H. Yang et al., "Improving urban land use and land cover classification from high-spatialresolution hyperspectral imagery using contextual information, " J. Appl. Remote Sens. 4(1), 041890 (2010).
4. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles, " IEEE Trans. Geosci. Remote Sens. 43(3), 480-491 (2005).
5. J. Qiong and D. A. L andgrebe, "Adaptive bayesian contextual classification based on markov random fields, " IEEE Trans. Geosci. Remote Sens. 40(11), 2454-2463 (2002).
6. N. P. Angelo and V. Haertel, "On the application of Gabor filtering in supervised image classification, " Int. J. Remote Sens. 24(10), 2167-2189 (2003).
7. R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Texture features for image classification, " IEEE Trans. Sys. Man Cybern. 3(6), 610-621 (1973).
8. A. N. Nyoungui, E. Tonye, and A. Akono, "Evaluation of speckle filtering and texture analysis methods for land cover classification from SAR images, " Int. J. Remote Sens. 23(9), 1895-1925 (2002).
9. F. Tsai et al., "3-D computation of gray level co-occurrence in hyperspectral image cubes, " Lec. Notes Comput. Sci. 4679, 429-440 (2007).
10. M. Fauvel et al., "Advances in spectral-spatial classification of hyperspectral images, " Proc. IEEE 101(3), 652-675 (2013).
11. P. Du et al., "Multiple cassifier system for remote sensing image classification: a review, " Sensors 12(4), 4764-4792 (2012).
12. T. G. Dietterich, "Ensemble methods in machine learning, " Lec. Notes Comput. Sci. 1857, 1-15 (2000).
13. Z.-H. Zhou, J. Wu, and W. Tang, "Ensembling neural networks: many could be better than all, " Artif. Intell. 137(122), 239-263 (2002).
14. L. I. Kuncheva, J. C. Bezdek, and R. P. Duin, "Decision templates for multiple classifier fusion: an experimental comparison, " Pattern Recogn. 34(2), 299-314 (2001).
15. K.Woods, W. P. Kegelmeyer, Jr., and K. Bowyer, "Combination of multiple classifiers using local accuracy estimates, " IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 405-410 (1997).
16. K. Tumer and J. Ghosh, "Error correlation and error reduction in ensemble classifiers, " Connection Sci. 8(3), 385-404 (1996).
17. G. Giacinto and F. Roli, "Adaptive selection of image classifiers, " Lec. Notes Comput. Sci. 1310, 38-45 (1997).
18. L. I. Kuncheva, "Switching between selection and fusion in combining classifiers: an experiment, " IEEE Trans. Syst. Man. Cybern. 32(2), 146-156 (2002).
19. A. H. R. Ko, R. Sabourin, and J. A. S. Britto, "From dynamic classifier selection to dynamic ensemble selection, " Pattern Recogn. 41(5), 1718-1731 (2008).
20. P. C. Smits, "Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection, " IEEE Trans. Geosci. Remote Sens. 40(4), 801-813 (2002).
21. L. Didaci et al., "A study on the performances of dynamic classifier selection based on local accuracy estimation, " Pattern Recogn. 38(11), 2188-2191 (2005).
22. Y. Tarabalka et al., "Multiple spectral-spatial classification approach for hyperspectral data, " IEEE Trans. Geosci. Remote Sens. 48(11), 4122-4132 (2010).
23. J. A. Benediktsson, J. Chanussot, and M. Fauvel, "Multiple classifier systems in remote sensing: from basics to recent developments, " Lec. Notes Comput. Sci. 4472, 501-512 (2007).
24. C. Chen et al., "Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine, " Remote Sens. 6(6), 5795-5814 (2014).
25. T. M. Nguyen and Q. M. J. Wu, "Gaussian-mixture-model-based spatial neighborhood relationships for pixel labeling problem, " IEEE Trans. Syst. Man. Cybern. 42(1), 193-202 (2012).
26. C. Chen et al., "Spectral-spatial preprocessing using multihypothesis prediction for noiserobust hyperspectral image classification, " IEEE J. Sel. Top. Appl. Earth Observ. 7(4), 1047-1059 (2014).
27. Q. Du et al., "Noise-adjusted principal component analysis for buried radioactive target detection and classification, " IEEE Trans. Nucl. Sci. 57(6), 3760-3767 (2010).
28. H. Yang et al., "An efficient method for supervised hyperspectral band selection, " IEEE Geosci. Remote Sens. Lett. 8(1), 138-142 (2011).
29. D. J. Marceau et al., "Evaluation of the grey-level co-occurrence matrix method, for landcover classification using SPOT imagery, " IEEE Trans. Geosci. Remote Sens. 28(4), 513-519 (1990).
30. W. R. Tobler, "A computer movie simulating urban growth in the Detroit region, " Econ. Geogr. 46(2), 234-240 (1970).
31. G. Giacinto and F. Roli, "Methods for dynamic classifier selection, " in Proc. 10th Int. Conf. on Image Analysis and Processing, pp. 659-664, IEEE, Venice, Italy (1999).
32. A. M. P. Canuto et al., "Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles, " Pattern Recogn. Lett. 28(4), 472-486 (2007).
33. K. H. Riitters, J. D. Wickham, and T. G. Wade, "An indicator of forest dynamics using a shifting landscape mosaic, " Ecol. Indicators 9(1), 107-117 (2009).
34. K. Liu et al., "Optical flow and principal component analysis-based motion detection in outdoor videos, " EURASIP J Adv. Signal Process. 2010, 680623 (2010).
35. H. Su et al., "Adaptive affinity propagation with spectral angle mapper for semi-supervised hyperspectral band selection, " Appl. Opt. 51(14), 2656-2663 (2012).
36. C. Chen et al., "Reconstruction of hyperspectral imagery from random projections using multihypothesis prediction, " IEEE Trans. Geosci. Remote Sens. 52(1), 365-374 (2014).
37. G. M. Foody, "Thematic map comparison: evaluating the statistical significance of differences in classification accuracy, " Photogramm. Eng. Remote Sens. 70(5), 627-633 (2004).