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Research Article Open Access

Improving Hyperspectral Image Classification with Watershed Segmentation-Based Texture Features

1Karadeniz Technical University
✉ Corresponding author: Volkan Yılmaz · volkanyilmaz.jdz@gmail.com
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References

  1. F. Tsai, and W. D. Philpot, “A derivative-aided hyperspectral image analysis system for land-cover classification,” IEEE T. Geosci. Remote Sens., vol. 40, no. 2, pp. 416-425, 2002.
  2. A. Hirano, M. Madden, and R. Welch, “Hyperspectral image data for mapping wetland vegetation,” Wetlands, vol. 23 no. 2, pp. 436-448, 2003.
  3. J. F. Knight, R. S. Lunetta, J. Ediriwickrema, and S. Khorram, “Regional scale land cover characterization using MODIS-NDVI 250 m multi-temporal imagery: A phenology-based approach,” GISci. Remote Sens., vol. 43, no. 1, pp. 1-23, 2006.
  4. B. Xu, and P. Gong, “Land-use/land-cover classification with multispectral and hyperspectral EO-1 data,” Photogramm. Eng. Remote Sens., vol. 73, no. 8, pp. 955-965, 2007.
  5. J. C. W. Chan, and D. Paelinckx, “Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery,” Remote Sens. Environ., vol. 112, no. 6, pp. 2999-3011, 2008.
  6. R. Duca, and F. Del Frate, “Hyperspectral and multiangle CHRIS– PROBA images for the generation of land cover maps,” IEEE T. Geosci. Remote Sens, vol. 46, no. 10, pp. 2857-2866, 2008.
  7. G. P. Petropoulos, K. Arvanitis, and N. “Sigrimis, Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping,” Expert Syst. Appl., vol. 39, no. 3, pp. 3800-3809, 2012.
  8. G. P. Petropoulos, C. Kalaitzidis, and K. P. Vadrevu, “Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery,” Comput. Geosci., vol. 41, pp. 99-107, 2012.
  9. I. Kanellopoulos, G. G. Wilkinson, and J. Megier, “Integration of neural network and statistical image classification for land cover mapping,” In: Geoscience and Remote Sensing Symposium (IGARSS'93), 1993, pp. 511-513.
  10. V. Yilmaz, B. Konakoglu, C. Serifoglu, O. Gungor, and E. Gökalp, “Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos,” Geocarto Int., vol. 33, no. 3, pp. 310-320, 2018.
  11. A. David, and B. Lerner, “Support vector machine-based image classification for genetic syndrome diagnosis,” Pattern Recognit. Lett., vol. 26, no. 8, pp. 1029-1038, 2005.
  12. M. Lauer, and S. Aswani, “Integrating indigenous ecological knowledge and multi-spectral image classification for marine habitat mapping in Oceania,” Ocean Coast. Manage., vol. 51, no. 6, pp. 495-504, 2008.
  13. T. Maekawa, T. Hara, and S. Nishio, “Image classification for mobile web browsing,” in: Proc. 15th International Conference on World Wide Web, 2006, pp. 43-52.
  14. B. Park, W. R. Windham, K. C. Lawrence, and D. P. Smith, “Hyperspectral image classification for fecal and ingesta identification by spectral angle mapper,” in: ASAE Annual Meeting, American Society of Agricultural and Biological Engineers, 2004.
  15. C. Fredembach, M. Schröder, and S. Süsstrunk, “Region-based image classification for automatic color correction,” in: Color and Imaging Conference, 2003, no. 1, pp. 59-65.
  16. J. Böhm, and C. Brenner, “Curvature-based range image classification for object recognition,” in: Intelligent Robots and Computer Vision XIX: Algorithms, Techniques, and Active Vision, 2000, vol. 4197, pp. 211-221.
  17. Q. Zhou, K. Yuan, H. Wang, and H. Hu, “Fpga-based colour image classification for mobile robot navigation,” in: IEEE International Conference on Industrial Technology, 2005, pp. 921-925.
  18. C. D. Lloyd, S. Berberoglu, P. J. Curran, and P. M. Atkinson, “A comparison of texture measures for the per-field classification of Mediterranean land cover,” Int. J. Remote Sens., vol. 25, no. 19, pp. 3943-3965, 2004.
  19. C. Serifoglu Yilmaz, E. Tunc Gormuş, and O. Gungor, “Texture Based Classification of Hyperspectral Images with Support Vector Machines Classifier” in: International Symposium on GIS Applications in Geography & Geosciences (ISGGG), 2017.
  20. V. N. Vapnik, The Nature of Statistical Learning Theory, 1995.
  21. Y. C. Ouyang, H. M. Chen, J. W. Chai, C. C. Chen, C. C. C. Chen, S. K. Poon, C. W. Yang, and S. K. Lee, “Independent component analysis for magnetic resonance image analysis,” EURASIP J. Adv. Signal Process., 2008.
  22. A. Tso, and P. M. Mather, Classification Methods for Remotely Sensed Data, 2009.
  23. S. Knerr, L. Personnaz, and G. Dreyfus, “Single-layer learning revisited: a stepwise procedure for building and training a neural network,” in: Neurocomputing, 1990, pp. 41-50.
  24. Y. Liu, and Y. F. Zheng, “One-against-all multi-class SVM classification using reliability measures,” Neural Netw., vol. 2, pp. 849-854, 2005.
  25. F. Melgani, and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE T. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, 2004.
  26. M. Pal, “Ensemble of support vector machines for land cover classification,” Int. J. Remote Sens., vol. 29, no. 10, pp. 3043-3049, 2008.
  27. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
  28. L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
  29. H. Digabel, and C. Lantuéjoul, “Iterative algorithms,” in: Proc. 2nd European Symposium on Quantitavive Analysis of Microstructures in Material Sciences, Biology and Medecine, pp. 39-49, 1977.
  30. H. Li, A. Elmoataz, J. M. Fadili, and S. Ruan, “An improved image segmentation approach based on level set and mathematical morphology,” in: 3rd International Symposium on Multispectral Image Processing and Pattern Recognition, vol. 5286, pp. 851-855, 2003.
  31. M. S. H. Khiyal, A. Khan, and A. Bibi, “Modified Watershed Algorithm for Segmentation of 2D Images,” Issues in Informing Science & Information Technology, 2009.
  32. N. Salman, Image segmentation based on watershed and edge detection techniques, Int. Arab. J. Inf. Techn., vol. 3, no. 2, pp. 104-110, 2006.
  33. K. Karantzalos, and D. Argialas, “Improving edge detection and watershed segmentation with anisotropic diffusion and morphological levellings,” Int. J. Remote Sens., vol. 27, no. 24, pp. 5427-5434, 2006.
  34. K. Haris, S. N. Efstratiadis, N. Maglaveras, and A. K. Katsaggelos, “Hybrid image segmentation using watersheds and fast region merging,” IEEE T. Image Process., vol. 7, no. 12, pp. 1684-1699, 1998.
  35. F. Meyer, and P. Maragos, “Multiscale morphological segmentations based on watershed, flooding, and eikonal PDE,” in: International Conference on Scale-Space Theories in Computer Vision, 1999, pp. 351-362.
  36. J. M. Gauch, Image segmentation and analysis via multiscale gradient watershed hierarchies, IEEE T. Image Process., vol. 8, no. 1, pp. 69-79, 1999.
  37. H. Anys, A. Bannari, D. C. He, and D. Morin, “Texture analysis for the mapping of urban areas using airborne MEIS-II images,” in: Proc. 1st International Airborne Remote Sensing Conference and Exhibition, 1994, vol. 3, pp. 231-245.
  38. Harris Geospatial Solutions, online help documentary.
  39. M. F. Baumgardner, L. L. Biehl, and D. A. Landgrebe, “220 band aviris hyperspectral image data set: June 12, 1992 indian pine test site 3,” Purdue University Research Repository, 2015.
  40. (2018) Datasets for Classification. [Online]. Available: http://lesun.weebly.com/hyperspectral-data-set.html
Research Contributions (2019+)Peer reviewed by at least two reviewers
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