非监督正交子空间投影的高光谱混合像元自动分解

利用混合像元线性分解技术处理高光谱影像，以获取研究区域中同一像元的不同组份是遥感应用的主要目的之一。近年来，研究者们发展了一种正交子空间投影技术(0SP)，用来探测感兴趣目标，进一步可以用来分解混合像元，然而应用这种方法分解混合像元的缺陷是需要有研究区域的先验信息，这就制约了它在这方面的应用。为此针对这种不足，提出一种非监督的正交子空间投影(UOSP)技术，用来自动获取影像端元光谱，同时进行混合像元分解。并用成像光谱数据(PHI)实例测试了这个方法，结果表明该方法自动获取的端元比较合理，且分解混合像元精度较高。

Unsupervised Orthogonal Subspace Projection Approach to Unmix Hyperspectral Imagery Automatically

Linear pixel unmixing is a straightforward and efficient approach to spectral decomposition of remotely sensed data. In recent years, Orthogonal subspace projection approach has been investigated and used in Linear pixel unmixing widely since it was proposed several years ago. A main drawback to its utilization in operational cases is that the spectral priori knowledge can not be automatically retrieved correctly and completely. To overcome the problem of not knowing the prior endmembers in an image dataset, this paper presents an unsupervised orthogonal subspace projection (UOSP) algorithm to retrieve endmember automatically at each time by searching the maximal pixel vector in an orthogonal imagery. If the pixel satisfied the property of being cohesive in spatial, it would be regarded as an endmember, then was removed the effect of it by orthogonal subspace projection method to get another orthogonal imagery. The experimental result shows that UOSP algorithm is an efficient and precise approach to retrieve endmembers and unmixing hybrid pixel automatically by employing PHI hyperspectral data.