摘要
通过波段选择可以显著提高高光谱遥感图像分类与解混的效率。提出了两种改进的线性预测(LP)波段选择方法,用图像的偏度或峰度度量波段信息量,结合互信息(MI)或K-L散度度量波段间的相似性,选择本身信息量大,且彼此间最不相似的两个波段作为初始波段,再通过改进的线性预测选择后续波段。噪声波段的存在会影响波段选择的效果,导致分类或解混精度低于预期。为了减弱噪声波段的不利影响,进一步提出噪声波段去除的方法,基于小波域的熵估计每波段的噪声,去除噪声较大的波段后进行波段选择。真实高光谱图像波段选择后分类和解混实验结果表明,改进的基于线性预测的波段选择方法能明显提高分类和解混的精度和效率,是一种有效的高光谱图像降维方法。
Band selection can greatly increase the efficiency of classification and unmixing of hyperspectral image. Two modified linear-prediction （LP） band selection methods based on similarity are proposed, which measure the information amount of bands through Skewness or Kurtosis and measure the similarity of bands through mutual information （MI） or K-L （Kullback-Leibler） divergence. The least similar two bands with large information amount are selected as the initial two bands, and the rest bands are selected by modified linear prediction. However, the existence of noise bands will affect the result of band selection, making the accuracy of classification or unmixing lower than expected. In order to weaken the adverse effect of noise bands, further efforts are made to estimate the noise of every band through wavelet entropy and remove the bands with considerable noise before band selection. The experiments of classification and unmixing after band selection for real hyperspectral images indicate that linear prediction based band selection can greatly increase the accuracy and efficiency of classification and unmixing , and it is an effective dimensionality reduction method for hyperspectral image.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2013年第8期256-263,共8页
Acta Optica Sinica
基金
国家自然科学基金(61171152)
教育部支撑技术项目(62510216)
浙江省自然科学基金(LY13F020044)
关键词
遥感
波段选择
线性预测
噪声去除
remote sensing
band selection
linear prediction
noise removing
