Random forest ensembles and extended multiextinction profiles for hyperspectral image classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 56, Issue 1, 2018, Pages 202-216

  Xia, Junshi ^a   Ghamisi, Pedram ^b,c   Yokoya, Naoto ^a   Iwasaki, Akira ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b GERMAN AEROSPACE CENTER DLR   (Germany)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

Ensemble learning; Extended multiextinction profiles (EMEPs); Hyperspectral image classification; Random forest (RF)

Indexed keywords

DECISION TREES; FEATURE EXTRACTION; HYPERSPECTRAL IMAGING; IMAGE ENHANCEMENT; INDEPENDENT COMPONENT ANALYSIS; ROTATION; SENSORS; SPECTROSCOPY; VEGETATION;

BOOSTING; ENSEMBLE LEARNING; EXTENDED MULTIEXTINCTION PROFILES (EMEPS); RADIO FREQUENCIES; RANDOM FORESTS;

IMAGE CLASSIFICATION;

EID: 85030640770     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2017.2744662     Document Type: Article

References (56)

1. M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, “Advances in spectral–spatial classification of hyperspectral images,” Proc. IEEE, vol. 101, no. 3, pp. 652–675, Mar. 2013.
2. C. I. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York, NY, USA: Plenum, 2003.
3. C. I. Chang, Hyperspectral Data Exploitation: Theory and Applications. Hoboken, NJ, USA: Wiley, 2007.
4. G. Hughes, “On the mean accuracy of statistical pattern recognizers,” IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55–63, Jan. 1968.
5. F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778–1790, Aug. 2004.
6. G. Camps-Valls and L. Bruzzone, “Kernel-based methods for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351–1362, Jun. 2005.
7. W. Liao, A. Pizurica, P. Scheunders, W. Philips, and Y. Pi, “Semisuper-vised local discriminant analysis for feature extraction in hyperspectral images,” IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 184–198, Jan. 2013.
8. M. Pal and G. M. Foody, “Feature selection for classification of hyperspectral data by SVM,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 5, pp. 2297–2307, May 2010.
9. V. N. Vapnik, The Nature of Statistical Learning Theory. New York, NY, USA: Springer, 1995.
10. Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on convolutional neural networks,” IEEE Geosci. Remote Sens. Lett., vol. 54, no. 10, pp. 6232–6251, Oct. 2016.
11. P. Ghamisi, Y. Chen, and X. X. Zhu, “A self-improving convolution neural network for the classification of hyperspectral data,” IEEE Geosci. Remote Sens. Lett., vol. 13, no. 10, pp. 1537–1541, Oct. 2016.
12. L. Breiman, “Random forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
13. J. Ham, Y. Chen, M. M. Crawford, and J. Ghosh, “Investigation of the random forest framework for classification of hyperspectral data,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 492–501, Mar. 2005.
14. M. Immitzer, C. Atzberger, and T. Koukal, “Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data,” Remote Sens., vol. 4, no. 9, pp. 2661–2693, 2012.
15. L. Guo, N. Chehata, C. Mallet, and S. Boukir, “Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests,” ISPRS J. Photogramm. Remote Sens., vol. 66, no. 1, pp. 56–66, Jan. 2011.
16. C. Pelletier, S. Valero, J. Inglada, N. Champion, and G. Dedieu, “Assessing the robustness of random forests to map land cover with high resolution satellite image time series over large areas,” Remote Sens. Environ., vol. 187, pp. 156–168, Dec. 2016.
17. E. Tuv, A. Borisov, G. C. Runger, and K. Torkkola, “Feature selection with ensembles, artificial variables, and redundancy elimination,” J. Mach. Learn. Res., vol. 10, pp. 1341–1366, Jul. 2009.
18. S. Schulter, P. Wohlhart, C. Leistner, A. Saffari, P. M. Roth, and H. Bischof, “Alternating decision forests,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Portland, OR, USA, Jun. 2013, pp. 508–515.
19. L. Breiman, “Bagging predictors,” Mach. Learn., vol. 24, no. 2, pp. 123–140, 1996.
20. Y. Freund and R. E. Schapire, “Experiments with a new boosting algorithm,” in Proc. Int. Conf. Mach. Learn., Bari, Italy, 1996, pp. 148–156.
21. T. K. Ho, “The random subspace method for constructing decision forests,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 8, pp. 832–844, Aug. 1998.
22. J. J. Rodriguez, L. I. Kuncheva, and C. J. Alonso, “Rotation forest: A new classifier ensemble method,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 10, pp. 1619–1630, Oct. 2006.
23. J. Xia, P. Du, X. He, and J. Chanussot, “Hyperspectral remote sensing image classification based on rotation forest,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 1, pp. 239–243, Jan. 2014.
24. J. Xia, M. Dalla Mura, J. Chanussot, P. Du, and X. He, “Random subspace ensembles for hyperspectral image classification with extended morphological attribute profiles,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 9, pp. 4768–4786, Sep. 2015.
25. J. Xia, N. Falco, J. A. Benediktsson, P. Du, and J. Chanussot, “Hyperspectral image classification with rotation random forest via KPCA,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 10, no. 4, pp. 1601–1609, Apr. 2017.
26. J. Besag, “Spatial interaction and the statistical analysis of lattice systems,” J. Roy. Statist. Soc. B (Methodol.), vol. 36, no. 2, pp. 192–236, 1974.
27. Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 11, pp. 1222–1239, Nov. 2001.
28. P. Ghamisi, J. A. Benediktsson, and M. O. Ulfarsson, “Spectral–spatial classification of hyperspectral images based on hidden Markov random fields,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2565–2574, May 2014.
29. Y. Tarabalka, M. Fauvel, J. Chanussot, and J. A. Benediktsson, “SVM- and MRF-based method for accurate classification of hyperspectral images,” IEEE Trans. Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 736–740, Oct. 2010.
30. J. Li, J. M. Bioucas-Dias, and A. Plaza, “Spectral–spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 3, pp. 809–823, Mar. 2012.
31. J. Xia, J. Chanussot, P. Du, and X. He, “Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and Markov random fields,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2532–2546, May 2015.
32. M. Pesaresi and J. A. Benediktsson, “A new approach for the morphological segmentation of high-resolution satellite imagery,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 2, pp. 309–320, Feb. 2001.
33. J. A. Benediktsson, M. Pesaresi, and K. Amason, “Classification and feature extraction for remote sensing images from urban areas based on morphological transformations,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 9, pp. 1940–1949, Sep. 2003.
34. M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, “Morphological attribute profiles for the analysis of very high resolution images,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 10, pp. 3747–3762, Oct. 2010.
35. M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, “Extended profiles with morphological attribute filters for the analysis of hyperspectral data,” Int. J. Remote Sens., vol. 31, no. 22, pp. 5975–5991, Jul. 2010.
36. P. Ghamisi, M. Dalla Mura, and J. A. Benediktsson, “A survey on spectral–spatial classification techniques based on attribute profiles,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2335–2353, May 2015.
37. J. A. Benediktsson and P. Ghamisi, Spectral–Spatial Classification of Hyperspectral Remote Sensing Images. Boston, MA, USA: Artech House, 2015, chs. 5–6.
38. P. Ghamisi, R. Souza, J. A. Benediktsson, X. X. Zhu, L. Rittner, and R. A. Lotufo, “Extinction profiles for the classification of remote sensing data,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 10, pp. 5631–5645, Oct. 2016.
39. P. Ghamisi, R. Souza, J. A. Benediktsson, L. Rittner, R. Lotufo, and X. X. Zhu, “Hyperspectral data classification using extended extinction profiles,” IEEE Trans. Geosci. Remote Sens., vol. 13, no. 11, pp. 1641–1645, Nov. 2016.
40. A. Hyvärinen and E. Oja, “Independent component analysis: Algorithms and applications,” Neural Netw., vol. 13, nos. 4–5, pp. 411–430, Jun. 2000.
41. P. Salembier, A. Oliveras, and L. Garrido, “Antiextensive connected operators for image and sequence processing,” IEEE Trans. Image Process., vol. 7, no. 4, pp. 555–570, Apr. 1998.
42. C. Vachier, “Extinction value: A new measurement of persistence,” in Proc. IEEE Workshop Nonlinear Signal Image Process., vol. 2. Jun. 1995, pp. 254–257.
43. P. Soille, Morphological Image Analysis: Principles and Applications, 2nd ed. Secaucus, NJ, USA: Springer-Verlag, 2003.
44. Y. Xu, T. Géraud, and L. Najman, “Morphological filtering in shape spaces: Applications using tree-based image representations,” in Proc. 21st Int. Conf. Pattern Recognit. (ICPR), Nov. 2012, pp. 485–488.
45. P. Monasse and F. Guichard, “Fast computation of a contrast-invariant image representation,” IEEE Trans. Image Process., vol. 9, no. 5, pp. 860–872, May 2000.
46. R. Souza, L. Rittner, R. Lotufo, and R. Machado, “An array-based node-oriented max-tree representation,” in Proc. IEEE Int. Conf. Image Process. (ICIP), Sep. 2015, pp. 3620–3624.
47. R. Souza, L. Rittner, R. Machado, and R. Lotufo, “A comparison between extinction filters and attribute filters,” in Proc. Int. Symp. Math. Morphol. Appl. Signal Image Process. (ISMM), Reykjavik, Iceland, May 2015, pp. 63–74.
48. G. Stiglic, J. J. Rodriguez, and P. Kokol, “Rotation of random forests for genomic and proteomic classification problems,” in Software Tools and Algorithms for Biological Systems, vol. 696. New York, NY, USA: Springer, 2011, pp. 211–221.
49. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. Hoboken, NJ, USA: Wiley, 2004.
50. C.-X. Zhang and J.-S. Zhang, “RotBoost: A technique for combining rotation forest and AdaBoost,” Pattern Recognit. Lett., vol. 29, no. 10, pp. 1524–1536, 2008.
51. L. I. Kuncheva and C. J. Whitaker, “Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy,” Mach. Learn., vol. 51, no. 2, pp. 181–207, 2003.
52. J. Xia, J. Chanussot, P. Du, and X. He, “Rotation-based support vector machine ensemble in classification of hyperspectral data with limited training samples,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 3, pp. 1519–1531, Mar. 2016.
53. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectral images using watershed transformation,” Pattern Recognit., vol. 43, no. 7, pp. 2367–2379, 2010.
54. Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 5, pp. 1267–1279, Oct. 2010.
55. Y. Tarabalka, “Classification of hyperspectral data using spectral-spatial approaches,” Ph.D. dissertation, Grenoble Inst. Technol., Univ. Iceland, Reykjavik, Iceland, 2010.
56. M. Dalla Mura, A. Villa, J. A. Benediktsson, J. Chanussot, and L. Bruzzone, “Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis,” IEEE Geosci. Remote Sens. Lett., vol. 8, no. 3, pp. 542–546, May 2011.