传统的高光谱混合像元分解方法仅考虑高光谱图像的几何特性或者丰度的稀疏性,而忽略高光谱数据的光谱空间特性。当原图像中不存在纯净像元时,分解精度将严重下降。为了解决这些问题,提出一种改进的空间信息约束非负矩阵分解的解混算法,该方法充分利用高光谱图像的空间信息和稀疏性,提高了传统非负矩阵分解算法的性能。合成的模拟图像和真实的高光谱图像实验表明,该方法克服了传统方法对噪声的敏感性及对纯像元的依赖性。

The traditional hyperspectral unmixing methods only consider the geological properties of hyperspectral images or the sparse properties of abundance and neglect the spectral spatial information of hyperspectral data. Thus when the pure pixels are missing, the unmixing accuracy is significantly reduced. In order to overcome these limitations, an improved spatial information constrained nonnegative matrix factorization method for unmixing is proposed. This method fully uses the spatial information and the sparse properties of hyperspectral images, and thus the properties of the traditional nonnegative matrix factorization methods are improved. Both the synthetic simulation images and the experimental results show that the proposed method has overcome the noise-sensitivity and the dependence on pure pixels of the traditional methods.