基于加权模糊C均值算法改进的高光谱图像分类方案设计

J4 ›› 2015, Vol. 32 ›› Issue (5): 539-549.

基于加权模糊C均值算法改进的高光谱图像分类方案设计

马欢,景志勇,陈明,张建伟

郑州轻工业学院软件学院，河南郑州，450002

收稿日期:2015-02-06 修回日期:2015-05-22 出版日期:2015-09-28 发布日期:2015-09-28
通讯作者: 马欢(1981－)河南孟州人，讲师，硕士，主要研究方向为信息处理，智能与信息系统。
基金资助:
国家自然科学基金项目(60974005)；河南省教育厅科学技术研究重点项目(13A520379)

Design of improved hyperspectral image classification scheme based on weighted fuzzy C means algorithm

Ma Huan, Jing Zhiyong,Chen Ming,Zhang Jianwei

Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou 450001, China

Received:2015-02-06 Revised:2015-05-22 Published:2015-09-28 Online:2015-09-28

摘要/Abstract

摘要：

为了有效改善高光谱图像数据分类的精确度，减少对大数目数据集的依赖，在原型空间特征提取方法的基础上提出一种基于加权模糊C均值算法改进型原型空间特征提取方案。该方案通过加权模糊 C 均值算法对每个特征施加不同的权重，从而保证提取后的特征含有较高的信息量。实验结果表明，与业内公认的原型空间提取算法相比该方案在相对较小的数据集下，其性能仍具有较为理想的稳定性，且具有相对较高的分类精度，这样子就大大降低了对数据集样本数量的依赖性，同时改善了原型空间特征方法的效率。

关键词: 高光谱图像, 数据分类, 特征提取, 加权模糊C 均值算法

Abstract:

In order to improve the classification accuracy of hyperspectral image data, reduce dependence on large number of data sets.An improved method based on weighted fuzzy C means algorithm is proposed for feature extraction of hyperspectral data in this paper,. The approach is an extension of previous approach—prototype space feature extraction. Each feature with different weights in terms of weighted fuzzy c means algorithm to ensure the features contain more information after extracted. Experiments results show that compared to results obtained from approach prototype spatial feature extraction method , this method has a stability to data set and higher classification accuracy when extracted a small number of features used to hyperspectral image data classification.

Key words: hyperspectral image, data classification, feature extraction, weighted fuzzy c-means

马欢景志勇陈明张建伟. 基于加权模糊C均值算法改进的高光谱图像分类方案设计[J]. J4, 2015, 32(5): 539-549.

Ma Huan, Jing Zhiyong,Chen Ming,Zhang Jianwei. Design of improved hyperspectral image classification scheme based on weighted fuzzy C means algorithm [J]. J4, 2015, 32(5): 539-549.

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