中文摘要：

传统的高光谱图像混合像元分解技术包括端元提取和估计每个端元的混合比例.虽然很多模型都能得到可以接受的解混结果,但是一些未知端元的存在使得结果在包含未知端元的像素点处出现偏差.因此,提出了一种基于支持向量数据描述的高光谱图像混合像元分解算法.首先高光谱图像数据被分成类内和类外两部分,类内是完全由已知端元数据混合的像素点,而类外数据是包含未知端元的像素点.两类数据交界处被认为是已知端元和未知端元混合的数据.然后再对这些像素点进行混合像元分解,分别对仿真数据和真实高光谱图像进行实验.结果表明该算法可以有效地解决因存在未知端元对解混精度的影响,而且能给出未知端元的解混分量.该方法的解混结果几乎不受未知端元的影响,优于直接解混结果.

英文摘要：

The traditional hyperspectral image unmixing algorithm involves the extraction of endmember and the estimation of abundance values for each endmember. Although many models usually provide acceptable unmixing results, the bias may be great in those pixels where an unknown endmember exists. Therefore, a hyperspectral image unmixing algorithm based on support vector data description （SVDD） was proposed. First, hyperspectral image datas were classified into two parts,i.e., inner-class and outer-class. The datas in the inner-class were considered as the pixels mixed by known endmember datas entirely, and the datas in outer-class included unknown endmembers. The boundary between the two classes was considered as points mixed by known and unknown endmember datas. Then, unmixing operation was carried out. Experimental results on synthetic and real hyperspectral data demonstrate that this method reduces effectively the influence of the existing unknown endmembers on unmixing results, and unmixing component with unknown endmember can be given. The results unmixed by the proposed algorithm are hardly affected by unknown endmembers and are superior to that of direct unmixing.