Liu Nana, Li Jingwen, Li Ninget al. Unsupervised classification approach based on graph-segment for multispectral remote sensing images[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(5): 544-546. (in Chinese)
Citation: Liu Nana, Li Jingwen, Li Ninget al. Unsupervised classification approach based on graph-segment for multispectral remote sensing images[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(5): 544-546. (in Chinese)

Unsupervised classification approach based on graph-segment for multispectral remote sensing images

  • Received Date: 10 Aug 2008
  • Publish Date: 31 May 2009
  • To solving the noisy points and high cost problems of pixel-based multispectral image classification, a hybrid unsupervised approach with graph-based segment and fuzzy c-means clustering was presented. First, based on the relationships among neighboring pixels, image was segmented into groups of sub-regions using the graph-based algorithm. Then according to the global feature vector of sub-region, the fuzzy c-means classifier was used to obtain the classification map. Experiments turn out that the proposed approach, which considers both relationships of neighboring pixels and global feature of sub-region, can achieve better accuracy and efficiency by comparing the result with pixel-based fuzzy c-means classification.

     

  • [1] Agrawal A, Kumar N, Radhakrishna M. Multispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation [J]. International Journal of Remote Sensing, 2007, 28(20):4597-4608 [2] Yamazaki T, Gingras D. Unsupervised multispectral image classification using MRF models and VQ Method [J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(2):1173-1175 [3] Flygare A M. Classification of remotely sensed data utilizing the autocorrelation between spatial-temporal neighbors . Umea · : Department of Mathematical Statistics, Umea · University, 1997 [4] Yu Jun, Magnus Ekstrm. Multispectral image classification using wavelets: a simulation study [J]. Pattern Recognition, 2003, 36(4):889-898 [5] Lee J Y, Warner T A. Segment based image classification [J]. International Journal of Remote Sensing, 2006, 27(16):3403-3412 [6] Felzenszwalb P, Huttenlocher D. Efficient graph-based image segmentation [J]. International Journal of Computer Vision, 2004, 59(2):167-181 [7] Shi J, Malik J. Normalized cuts and image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8):888-905 [8] Kanungo T, Mount D M, Netanyahu N S, et al. An efficient k-means clustering algorithm: analysis and implementation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7):881-892
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