基于光谱最佳尺度分割特征的高光谱混合像元分解

Unmixing of Hyperspectral Mixture Pixels Based on Spectral Multiscale Segemented Features

提出一种基于光谱多尺度分割特征的混合像元分解方法。首先在分割段内离差平方和最小准则下,对高光谱影像的光谱进行多尺度分割,并提取以各分割段中对应像元的光谱平均值的光谱特征。多尺度特征提取可以通过多尺度特征分析,选取最佳尺度特征或者不同分割尺度下的光谱特征组合,从而进行混合像元的限制性分解。利用模拟的与真实的数据进行验证,结果表明,本文方法能够有效地提高遥感影像混合像元的分解精度,并且显著优于光谱维小波特征的分解方法。

A novel approach to improve abundance estimation of hyper-spectral image using spectral piecewise constant features is presented.Firstly it extracts the spectral features by partitioning the spectral signals into a fixed number of contiguous intervals with constant intensities in terms of minimizing the mean square error.Then the multi-scale features can be determined by using optimal segmented scale or combination of features with different segmentation scales.In the end,the estimation is performed by unmixing the pixel in the feature space with constrained least square algorithm to achieve the respective abundance fractions of these end-members present in the pixel.Algorithm validation and comparison were done with simulated and real data.Experimental results demonstrate the proposed method can significantly improve the least squares estimation of end-member abundances using remotely sensed hyper-spectral signals,as compared to those of original hyper-spectral signals or discrete wavelet transform based features.