基于粗糙集和模糊聚类的超谱波段约简

石红; 沈毅; 刘志言

基于粗糙集和模糊聚类的超谱波段约简

计量
- 文章访问数: 2429
- HTML全文浏览量: 118
- PDF下载量: 711
- 被引次数: 0
出版历程
- 收稿日期: 2002-12-13
- 修回日期: 2003-04-28
- 刊出日期: 2004-04-19

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

摘要

摘要: 由于超光谱图像数据量大,维数高给分类识别处理带来不便,该文提出一种可行有效的波段约简方法.通过FCM聚类将原始波段划分为若干等价波段组,然后根据最大隶属度原则只保留每组中具有代表性的波段,达到维数减小的目的。其中,模糊聚类中相似度的定义是基于超谱相邻波段间的相关性,利用粗糙集理论中的处理属性依赖性的方法合理表达出来。实验表明,这一方法既有效地缩减了高维数据,又尽可能少地损失有用信息,保持了原始波段的分类能力。
- 超谱遥感; 粗糙集; 模糊聚类; 波段约简
Abstract: 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.

HTML全文

参考文献(1)

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.

施引文献

资源附件(0)

访问统计