Advanced Search
Volume 26 Issue 4
Apr.  2004
Turn off MathJax
Article Contents
Shi Hong, Shen Yi, Liu Zhi-yan. Hyperspectral Band Reduction Based on Rough Sets and Fuzzy C-Means Clustering[J]. Journal of Electronics & Information Technology, 2004, 26(4): 619-624.
Citation: Shi Hong, Shen Yi, Liu Zhi-yan. Hyperspectral Band Reduction Based on Rough Sets and Fuzzy C-Means Clustering[J]. Journal of Electronics & Information Technology, 2004, 26(4): 619-624.

Hyperspectral Band Reduction Based on Rough Sets and Fuzzy C-Means Clustering

  • Received Date: 2002-12-13
  • Rev Recd Date: 2003-04-28
  • Publish Date: 2004-04-19
  • A method of hyperspectral baud reduction based on Rough Sets (RS) and Fuzzy C-Means (FCM) clustering is proposed, which consists of the following two steps. First, Fuzzy C-Means clustering algorithm is used to classify the original bands into equivalent band groups, which employs the concept of attribute dependency defined in RS to define the distance between a group and the cluster center, viz. the correlatives of adjacent bands. Then the data is reduced by selecting the only one from each group with maximum grade of fuzzy membership. With this approach, great dimension of band is decreased while preserving much wanted information. Simulation results prove the effectiveness of this approach.
  • loading
  • G.P. Abousleman, et al.. Hyperspectral image compression using entropy-constrained predictive trellis coded quantization. IEEE Trans. on Image Processing, 1997, IP-6(4): 566-573.[2]Ryan M J, Arnold J F. The lossless compression of AVIRIS images by vector quantization. IEEE Trans. on Geoscience and Remote Sensing, 1997, GRS-35(3): 546-550.[3]Jimenez L O, Landgrebe D. A supervised classification in high-dimensional space: geometrical,statistical, and asymptotical properties of multivariate data. IEEE Trans. on System, Man, and Cybernetics-Part C: Applications and Reviews, 1998, SMC-C-28(1): 39-54.[4]Jia Xiuping, Richards J A. Segmented principal componemts transformation for efficient hyperspectral remote-sensing image display and classification. IEEE Trans. on Geoscience and Remote Sensing, 1999, GRS-37(1): 538-542.[5]Zhang Ye.[J].Desai M D, Zhang Junping, et al.. Adaptive subspace decomposition for hyperspectral data dimensionality reduction. International Conference on Image Processing (ICIP99), Kobe,Japan.1999,:-[6]Tu Te-Ming, Chen Chin-Hsing. A fast two stage classification method for high dimensional remote sensing data. IEEE Trans. on Geoscience and Remote Sensing, 1998, GRS-36(1): 182-191.[7]Morgan J T, Henneguelle A, Crawford M M, et al.. Best bases Bayesian hierarchical classifier for hyperspectral data analysis[J].Geoscience and Remote Sensing Symposium, IGARSS02, Toronto,Canada, 2002 IEEE International.2002, Vol.3:1434-1437[8]Esposito P G, Bartoloni A. An application of genetic algorithms to the geometric correction of HypSEO hyperspectral data[J].Geoscience and Remote Sensing Symposium, IGARSS02, Toronto,Canada, 2002 IEEE International.2002, Vol.6:3507-3509[9]Kaewpijit S, Le Moigne J, E1-Ghazawi T. A wavelet-based PCA reduction for hyperspectral imagery[J].Geoscience and Remote Sensing Symposium, IGARSS02, Toronto, Canada, 2002 IEEE International.2002, Vol.5:2581-2583[10]Hsu Pai-Hui, Tseng Yi-Hsing. Feature extraction of hyperspectral data using the best wavelet packet basis[J].Geoscience and Remote Sensing Symposium, IGARSS02, Toronto, Canada, 2002 IEEE International.2002, Vol.3:1667-1669[11]Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer Academic Pub., 1991: Ch.2-5.[12]Pawlak Z, et al.. Rough sets[J].Communications of the ACM.1995, 38(11):89-95[13]Kerber R. ChiMerge: discretization of numeric attributes. in Proc. 10th National Conference on Artificial Intelligence, San Jose, CA, 1992: 123-127.[14]Dunn J C. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters[J].Journal of Cybernetics.1973, 3(3):32-57[15]Bezdek C, Dunn J C. Optimal fuzzy partitions: a heuristic for estimating the parameters in a mixture of normal distributions. IEEE Trans. on Comput., 1986, C-35(8): 935-938.[16]孙立新,高文.基于粗糙集的遥感优化分类波段选择.模式识别与人工智能,2000,13(2):181-186.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (2432) PDF downloads(711) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return