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有效信息量,从而达到减少训练数据集而不降低分类所需信息量的效果。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。
In order to improve the classification accuracy of hyperspectral image data and reduce its dependence on a large number of data sets, we propose an improved feature extraction scheme based on weighted fuzzy C-means algorithm. The weighted fuzzy C-means algorithm is applied to assign different weights to each feature, thus ensuring the extracted features contain more effective information so as to reduce the number of training data sets without reducing the amount of information needed for classification. Experimental results show that compared with the prototype spatial feature extraction method, the proposed method is stable and has a higher classification accuracy under small data sets.
出处
《计算机工程与科学》
CSCD
北大核心
2016年第7期1462-1470,共9页
Computer Engineering & Science
基金
国家自然科学基金青年项目(71301108)
辽宁省科学十二五规划课题(JG13DB093)
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
hyperspectral image
data classification
feature extraction
weighted fuzzy C-means algorithm
