Song Ahram, Chang Anjin, Choi Jaewan, Choi Seokkeun, Kim Yongil
Department of Civil and Environmental Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea.
School of Earth and Environmental Sciences, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea.
Sensors (Basel). 2015 Jan 23;15(2):2593-613. doi: 10.3390/s150202593.
Pure surface materials denoted by endmembers play an important role in hyperspectral processing in various fields. Many endmember extraction algorithms (EEAs) have been proposed to find appropriate endmember sets. Most studies involving the automatic extraction of appropriate endmembers without a priori information have focused on N-FINDR. Although there are many different versions of N-FINDR algorithms, computational complexity issues still remain and these algorithms cannot consider the case where spectrally mixed materials are extracted as final endmembers. A sequential endmember extraction-based algorithm may be more effective when the number of endmembers to be extracted is unknown. In this study, we propose a simple but accurate method to automatically determine the optimal endmembers using such a method. The proposed method consists of three steps for determining the proper number of endmembers and for removing endmembers that are repeated or contain mixed signatures using the Root Mean Square Error (RMSE) images obtained from Iterative Error Analysis (IEA) and spectral discrimination measurements. A synthetic hyperpsectral image and two different airborne images such as Airborne Imaging Spectrometer for Application (AISA) and Compact Airborne Spectrographic Imager (CASI) data were tested using the proposed method, and our experimental results indicate that the final endmember set contained all of the distinct signatures without redundant endmembers and errors from mixed materials.
由端元表示的纯净地表材料在各个领域的高光谱处理中起着重要作用。已经提出了许多端元提取算法（EEA）来寻找合适的端元集。大多数涉及在没有先验信息的情况下自动提取合适端元的研究都集中在N-FINDR上。尽管有许多不同版本的N-FINDR算法，但计算复杂性问题仍然存在，并且这些算法无法考虑将光谱混合材料作为最终端元提取的情况。当要提取的端元数量未知时，基于顺序端元提取的算法可能更有效。在本研究中，我们提出了一种简单而准确的方法，使用这种方法自动确定最佳端元。所提出的方法包括三个步骤，用于确定端元的适当数量，并使用从迭代误差分析（IEA）获得的均方根误差（RMSE）图像和光谱判别测量来去除重复或包含混合特征的端元。使用所提出的方法对合成高光谱图像和两种不同的航空图像（如用于应用的航空成像光谱仪（AISA）和紧凑型航空光谱成像仪（CASI）数据）进行了测试，我们的实验结果表明，最终的端元集包含了所有不同的特征，没有冗余端元和来自混合材料的误差。
