IEEE Trans Image Process. 2014 Dec;23(12):5510-8. doi: 10.1109/TIP.2014.2362056.
This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one and nonnegativity constraints. Two models with increasing complexity are developed to achieve this challenging task, depending on how noise interacts with hyperspectral data. The first one leads to a convex optimization problem and is solved with the alternating direction method of multipliers. The second one accounts for signal-dependent noise and is addressed with a reweighted least squares algorithm. Experiments on synthetic and real data demonstrate the effectiveness of our approach.
本文解决了高光谱图像的盲目和完全约束解混问题。解混过程无需使用任何字典，并假设场景中组成物质的数量及其光谱特征未知。估计的丰度满足所需的总和为一和非负约束。根据噪声与高光谱数据的相互作用，开发了两个具有递增复杂性的模型来实现这一具有挑战性的任务。第一个模型导致凸优化问题，并使用交替方向乘子法求解。第二个模型考虑了与信号相关的噪声，并采用了重加权最小二乘算法进行处理。在合成和真实数据上的实验验证了我们方法的有效性。
