摘要
在遥感数据处理研究中,高维高光谱数据的冗余信息和噪声严重影响高光谱数据的分类精度,针对此问题提出基于互信息波段选择和经验模态分解的高精度高光谱数据分类算法(M-IEMD-SVM)。分别采用基于互信息波段选择方法和经验模态分解实现对高光谱数据的冗余信息处理和特征提取,并获得处理后的高光谱数据X″。采用支持向量机分类算法对处理后的高光谱数据X″进行分类实验。仿真实验结果证实MI-EMD-SVM算法不仅提高高光谱数据分类精度,同时还减少支持向量数目,提高高光谱数据分类速度。
In remote-sensing data processing research, redundant information and noise of high-dimensional hyperspectral data affect the classification accuracy of hyperspectral data seriously. To solve this problem, we propose an algorithm of hyperspectral data classification based on band selection with mutual information and empirical mode decomposition （MI-EMD-SVM）. Band selection based on mutual information is used to achieve redundant information processing, and empirical mode decomposition （EMD） is used to achieve feature extraction. And the obtained hyperspectral data X″ has been processed. The support vector machines （SVM） classification of the data is classified, which has been processed. Experimental results of the AVIRIS data indicate that the proposed approach improves the classification accuracy of hyperspectral data, significantly reduces the number of support vector, and improves the speed of hyperspectral data classification.
出处
《激光与光电子学进展》
CSCD
北大核心
2011年第9期59-66,共8页
Laser & Optoelectronics Progress
基金
国家自然科学基金(60975009)资助课题
关键词
图像处理
高光谱数据
分类
互信息
波段选择
经验模态分解
分类精度
image processing
hyperspectral data
classification
mutual information
band selection
empirical mode decomposition
classification accuracy
