在高光谱遥感图像中, 地物的空间分布往往呈现两种特征: 一是都有各自的主导区域；二是在地表空间上分布连续.利用这两种先验信息, 分别引入了对丰度的正交约束与平滑约束, 提出了一种基于丰度约束的非负矩阵分解算法.为进一步地提高算法的性能, 另外还提出了一种新的算法停止准则及权重因子调整策略, 以适应信噪比以及像元混合程度的变化.在仿真数据和实测数据上的实验结果表明, 该算法不仅能很好地表征地物的分布特征, 提高解混精度, 而且在信噪比较低, 无纯像元的条件下, 仍然能得到较好的解混结果.

In hyperspectral remote sensing imagery, material usually present two spatial distribution characteristics: one is its dominance in some special areas, another is its consistency on the land surface. By utilizing this two prior information, we propose an algorithm named nonnegative matrix factorization (NMF) with abundance constraint, which introduces both orthogonality and smoothness into abundance. To further improve the algorithm performance, we also propose a new stop criterion and an adjusting method of adapting weight factor to the varying signal-to-noise (SNR) and mixing degree. Experimental results based on synthetic and real hyperspectral data show that our algorithm not only represents material distribution characteristics very well, but also increases the unmixing accuracy. Meanwhile, the algorithm can lead to satisfactory unmixing results under the conditions of low SNR and no pure pixels.