光子学报, 2010, 39 (1): 188, 网络出版: 2010-03-11
基于均值漂移和模糊积分融合的高光谱图像分割
Hyperspectral Image Segmentation Based on Mean Shift and Fuzzy Integral Fusion
高光谱图像 图像分割 特征降维 均值漂移 模糊积分 Hyperspectral image Image segmentation Feature dimension reduction Mean shift Fuzzy integral
摘要
针对高光谱图像的特殊性,给出了一种基于均值漂移和模糊积分融合的高光谱图像分割算法.依据高光谱图像各波段间高度的相关性将其分为若干组,通过主成分分析对各波段子集进行降维.在此基础上,采用均值漂移算法计算各波段子集图像的聚类中心进而实现分割,再利用模糊积分融合各波段子集的分割结果.仿真结果证明了该算法的有效性.
Abstract
A new segmentation method based on mean shift and fuzzy integral algorithm is proposed to overcome the problems of hyperspectral image processing.A hyperspectral image is grouped into several band subset images according to their correlation relationship.Then,the dimension of each subset band image is reduced by principle component analysis.To segment each subset band image quickly,the mean shift algorithm is employed to find the cluster centers.And,the segmentation results are fused by fuzzy integral.Simulation results show the effectiveness of this method.
王凯, 赵永强, 程咏梅, 魏坤. 基于均值漂移和模糊积分融合的高光谱图像分割[J]. 光子学报, 2010, 39(1): 188. WANG Kai, ZHAO Yong-qiang, CHENG Yong-mei, Wei Kun. Hyperspectral Image Segmentation Based on Mean Shift and Fuzzy Integral Fusion[J]. ACTA PHOTONICA SINICA, 2010, 39(1): 188.
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