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基于粗糙集和模糊聚類的超譜波段約簡

石紅 沈毅 劉志言

石紅, 沈毅, 劉志言. 基于粗糙集和模糊聚類的超譜波段約簡[J]. 電子與信息學(xué)報, 2004, 26(4): 619-624.
引用本文: 石紅, 沈毅, 劉志言. 基于粗糙集和模糊聚類的超譜波段約簡[J]. 電子與信息學(xué)報, 2004, 26(4): 619-624.
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.

基于粗糙集和模糊聚類的超譜波段約簡

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

  • 摘要: 由于超光譜圖像數(shù)據(jù)量大,維數(shù)高給分類識別處理帶來不便,該文提出一種可行有效的波段約簡方法.通過FCM聚類將原始波段劃分為若干等價波段組,然后根據(jù)最大隸屬度原則只保留每組中具有代表性的波段,達到維數(shù)減小的目的。其中,模糊聚類中相似度的定義是基于超譜相鄰波段間的相關(guān)性,利用粗糙集理論中的處理屬性依賴性的方法合理表達出來。實驗表明,這一方法既有效地縮減了高維數(shù)據(jù),又盡可能少地損失有用信息,保持了原始波段的分類能力。
  • 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]孫立新,高文.基于粗糙集的遙感優(yōu)化分類波段選擇.模式識別與人工智能,2000,13(2):181-186.
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出版歷程
  • 收稿日期:  2002-12-13
  • 修回日期:  2003-04-28
  • 刊出日期:  2004-04-19

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