Combining Rotation Forest and Multiscale Segmentation for the Classification of Hyperspectral Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 9, Issue 9, 2016, Pages 4060-4072

  Chen, Jike ^a   Xia, Junshi ^b   Du, Peijun ^a   Chanussot, Jocelyn ^c,d  

^a NANJING UNIVERSITY   (China)

^b UNIVERSITÉ DE BORDEAUX   (France)

^c GIPSA LAB   (France)

^d UNIVERSITY OF ICELAND   (Iceland)

Author keywords

Classification; hyperspectral data; multiscale (MS) segmentation; rotation forest (RoF)

Indexed keywords

FORESTRY; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; SPECTROSCOPY;

CLASSIFICATION PERFORMANCE; CLASSIFICATION SCHEME; HYPER-SPECTRAL IMAGES; MAJORITY VOTING RULES; MULTIPLE CLASSIFICATION; MULTISCALE SEGMENTATION; SPATIAL INFORMATION MODELS; SPATIAL INFORMATIONS;

CLASSIFICATION (OF INFORMATION);

ACCURACY ASSESSMENT; DATA SET; EXPERIMENTAL STUDY; IMAGE CLASSIFICATION; NUMERICAL MODEL; SEGMENTATION; SPECTRAL RESOLUTION;

EID: 84959269276     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2016.2524517     Document Type: Article

References (50)

1. A. Plaza et al., "Recent advances in techniques for hyperspectral image processing, " Remote Sens. Environ., vol. 113, pp. S110-S122, 2009.
2. X. Shang and L. A. Chisholm, "Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 2481-2489, Jun. 2014.
3. J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. M. Nasrabadi, and J. Chanussot, "Hyperspectral remote sensing data analysis and future challenges, " IEEE Geosci. Remote Sens. Mag., vol. 1, no. 2, pp. 6-36, Jun. 2013.
4. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. Hoboken, NJ, USA: Wiley, 2003, vol. 29.
5. G. Hughes, "On the mean accuracy of statistical pattern recognizers, " IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55-63, Jan. 1968.
6. X. Jia, B.-C. Kuo, and M. M. Crawford, "Feature mining for hyperspectral image classification, " Proc. IEEE, vol. 101, no. 3, pp. 676-697, Mar. 2013.
7. 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.
8. G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Marí, J. Vila-Francés, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification, " IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 93-97, Jan. 2006.
9. 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.
10. J.-M. Yang, B.-C. Kuo, P.-T. Yu, and C.-H. Chuang, "A dynamic subspace method for hyperspectral image classification, " IEEE Trans. Geosci. Remote Sens., vol. 48, no. 7, pp. 2840-2853, Jul. 2010.
11. S. Kawaguchi and R. Nishii, "Hyperspectral image classification by bootstrap AdaBoost with random decision stumps, " IEEE Trans. Geosci. Remote Sens., vol. 45, no. 11, pp. 3845-3851, Nov. 2007.
12. 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.
13. 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.
14. J. Xia, J. Chanussot, P. Du, and X. He, "Rotation-based ensemble classifiers for high-dimensional data, " in Fusion in Computer Vision. New York, NY, USA: Springer, 2014, pp. 135-160.
15. P. Du, J. Xia, W. Zhang, K. Tan, Y. Liu, and S. Liu, "Multiple classifier system for remote sensing image classification: A review, " Sensors, vol. 12, no. 4, pp. 4764-4792, 2012.
16. R. L. Kettig and D. Landgrebe, "Classification ofmultispectral image data by extraction and classification of homogeneous objects, " IEEE Trans. Geosci. Electron., vol. GE-14, no. 1, pp. 19-26, Jan. 1976.
17. Y. Tarabalka, M. Fauvel, J. Chanussot, and J. A. Benediktsson, "SVMand MRF-based method for accurate classification of hyperspectral images, " IEEE Geosci. Remote Sens. Lett., vol. 7, no. 4, pp. 736-740, Oct. 2010.
18. S. Van der Linden, A. Janz, B. Waske, M. Eiden, and P. Hostert, "Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines, " J. Appl. Remote Sens., vol. 1, no. 1, p. 013543, 2007.
19. M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 11, pp. 3804-3814, Nov. 2008.
20. G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Marí, J. Vila-Francés, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification, " IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 93-97, Jan. 2006.
21. R. L. Kettig and D. Landgrebe, "Classification ofmultispectral image data by extraction and classification of homogeneous objects, " IEEE Trans. Geosci. Electron., vol. GE-14, no. 1, pp. 19-26, Jan. 1976.
22. 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.
23. 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.
24. Y. Tarabalka, J. A. Benediktsson, and J. Chanussot, "Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques, " IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2973-2987, Aug. 2009.
25. K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot, and J. A. Benediktsson, "Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach, " IEEE Trans. Image Process., vol. 21, no. 4, pp. 2008-2021, Apr. 2012.
26. B. Johnson and Z. Xie, "Unsupervised image segmentation evaluation and refinement using a multi-scale approach, " ISPRS J. Photogramm. Remote Sens., vol. 66, no. 4, pp. 473-483, 2011.
27. U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, "Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information, " ISPRS J. Photogramm. Remote Sens., vol. 58, no. 3, pp. 239-258, 2004.
28. C. Coburn and A. C. B. Roberts, "A multiscale texture analysis procedure for improved forest stand classification, " Int. J. Remote Sens., vol. 25, no. 20, pp. 4287-4308, 2004.
29. Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, "Multiple spectral-spatial classification approach for hyperspectral data, " IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4122-4132, Nov. 2010.
30. 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.
31. H. Jiawei and M. Kamber, Data Mining: Concepts and Techniques. San Mateo, CA, USA: Morgan Kaufmann, 2001, vol. 5.
32. L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees. Boca Raton, FL, USA: CRC Press, 1984.
33. M. Baatz and A. Schäpe, "Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation, " in Angewandte Geographische Informationsverarbeitung XII. Karlsruhe, Germany: Wichmann Verlag, 2000, pp. 12-23.
34. C. Evans, R. Jones, I. Svalbe, and M. Berman, "Segmenting multispectral landsat TM images into field units, " IEEE Trans. Geosci. Remote Sens., vol. 40, no. 5, pp. 1054-1064, May 2002.
35. A. Rydberg and G. Borgefors, "Integrated method for boundary delineation of agricultural fields in multispectral satellite images, " IEEE Trans. Geosci. Remote Sens., vol. 39, no. 11, pp. 2514-2520, Nov. 2001.
36. Y. Ke, L. J. Quackenbush, and J. Im, "Synergistic use of Quickbird multispectral imagery and LIDAR data for object-based forest species classification, " Remote Sens. Environ., vol. 114, no. 6, pp. 1141-1154, 2010.
37. J. Im, J. Jensen, and J. Tullis, "Object-based change detection using correlation image analysis and image segmentation, " Int. J. Remote Sens., vol. 29, no. 2, pp. 399-423, 2008.
38. G. Mallinis, N. Koutsias, M. Tsakiri-Strati, and M. Karteris, "Objectbased classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site, " ISPRS J. Photogramm. Remote Sens., vol. 63, no. 2, pp. 237-250, 2008.
39. A. G. Definiens, eCognition Version 4 User Guide. München, Germany: Definiens Imaging GmbH, 2004.
40. G. Meinel and M. Neubert, "A comparison of segmentation programs for high resolution remote sensing data, " Int. Arch. Photogramm. Remote Sens., vol. 35, pp. 1097-1105, 2004.
41. Z. Zhen, L. J. Quackenbush, S. V. Stehman, and L. Zhang, "Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification, " Int. J. Remote Sens., vol. 34, no. 19, pp. 6914-6930, 2013.
42. M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 11, pp. 3804-3814, Nov. 2008.
43. 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.
44. X. Kang, S. Li, and J. A. Benediktsson, "Feature extraction of hyperspectral images with image fusion and recursive filtering, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 6, pp. 3742-3752, Jun. 2014.
45. X. Kang, S. Li, and J. A. Benediktsson, "Spectral-spatial hyperspectral image classification with edge-preserving filtering, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 5, pp. 2666-2677, May 2014.
46. J. Xia, J. Chanussot, P. Du, and X. He, "Rotation-based SVM in classification of hyperspectral data with limited training samples, " IEEE Trans. Geosci. Remote Sens. [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7299279, to be published.
47. J. Xia, W. Liao, J. Chanussot, P. Du, G. Song, andW. Philips, "Improving random forest with ensemble of features and semisupervised feature extraction, " IEEE Geosci. Remote Sens. Lett., vol. 12, no. 7, pp. 1471-1475, Jul. 2015.
48. Y. Gu, C. Wang, D. You, Y. Zhang, S. Wang, and Y. Zhang, "Representative multiple kernel learning for classification in hyperspectral imagery, " IEEE Trans. Geosci. Remote Sens., vol. 50, no. 7, pp. 2852-2865, Jul. 2012.
49. X. Huang and L. Zhang, "An SVM ensemble approach combining spectral, structural, and semantic features for the classification of highresolution remotely sensed imagery, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 257-272, Jan. 2013.
50. X. Huang, Q. Lu, and L. Zhang, "A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas, " ISPRS J. Photogramm. Remote Sens., vol. 90, pp. 36-48, 2014.