基于光谱维小波特征的混合像元投影迭代分解

Projective Iterative Unmixing of Hyperspectral Image Based on Spectral Domain Wavelet Feature

混合像元线性分解是高光谱遥感应用的关键技术之一.本文利用小波变换多分辨率分析的特点,提出了一种以小波低频系数为特征的混合像元投影迭代分解的方法.首先利用离散二进小波提取了高光谱影像特征,再基于影像特征,用投影迭代方法自动确定出端元光谱,并以限制性的最小二乘方法估计出混合像元的组分.实验结果表明,本文方法能够较大的提高遥感影像混合像元的分解精度.

Linear pixel unmixing is one of the key technologies for hyperspectral image application. However, there are two problems for the hyperspectral decomposition in operational cases. One is the endmembers of an image can＇ t be extracted automatically with traditional supervised ways;the other is unmixing hundreds of spectral bands directly may reduce accuracies due to the high correlation between bands. To mitigate the problems, we proposed a method for abundance estimation from spectral domain wavelet features. We utilized the discrete wavelet transform （DWT） as a preprocessing step for the feature extraction, then selected endmembers with projective iterative algorithm in an unsupervised fashion based on the features. In the end, we performed a constrained least square method for the abundance estimation. Algorithm validation and comparison were done with real PHI data. Experimental results show that the use of DWT-based features can improve the abundance estimation, as compared to those of original hyperspectral signals or conventional PCA-based features.