摘要
目的超光谱图像具有极高的谱间分辨率,巨大的数据量给分类识别等后续处理带来很大压力。为了有效降低图像数据维数,提出基于分段2维主成分分析(2DPCA)的超光谱图像波段选择算法。方法首先根据谱间相关性对原始图像进行波段分组,然后根据主成分反映每个光谱波段的信息比重分别对每组图像进行波段选择,从而实现超光谱图像的谱间降维。结果该算法有效降低了超光谱图像的光谱维数,选择的波段明显反映出不同地物像元矢量的区别。结论实验结果表明,该波段选择算法相对传统算法速度更快,并且较好地保留了原始图像的局部重要信息,对后续处理有积极意义。
Objective It is well known that hyperspectral remote sensing technique is one of the breakthroughs in the earth observation. Hyperspectral images have the characteristics of contiguous spectral range and narrow spectrum interval. Typi- cally, each image pixel is represented by hundreds of values, corresponding to various wavelengths. Today, with the wide application of hyperspectral images in many fields, such as surveillance, geology, environmental monitoring, and meteorol- ogy, the high dimensionality and huge amount data has become a key problem. Feature selection, especially band selec- tion, plays an important role in hyperspectral image processing. In order to achieve the reduction of inter-spectral dimen- sionality effectively, a segmented two-dimensional principal component analysis （2DPCA） algorithm for band selection of hyperspectral image is proposed. Method The proposed algorithm is based on the traditional 2DPCA feature extraction method. It combines the advantages of 2DPCA and band selection. The whole band selection process can be divided into two steps. First, the spectral bands in a hyperspectral image are grouped into different clusters based on the correlation co- efficient between them. Then, a band selection based on 2DPCA is taken for the bands in each group separately. In the second step, the image data dimensionality is converted in order to get a covariance matrix co^esponding to the number of bands in each group. The number of selected bands is determined by the cumulative contribution rate of principal compo- nents. The bands are selected according to the amount of information of each band mapped into the principal components. This method has many advantages. It only needs to calculate all of the eigenvalues and eigenvectors of the covariance ma- trix, while it does not have to transform the original image matrix. Therefore it has a small amount of calculations and will not change the physical characteristics of the original image. Result According to the inter-spectral correlation
出处
《中国图象图形学报》
CSCD
北大核心
2014年第2期328-332,共5页
Journal of Image and Graphics
基金
吉林省科技厅基金项目(20090512
20100312)
关键词
超光谱图像
2维主成分分析
波段选择
波段分组
hyperspectral image
two-dimensional principal component analysis （2DPCA）
band selection
band grouping
