CAJ | 학술논문

端元提取是混合像元分解的关键，研究其算法在高精度的地物识别、丰度反演和定量遥感等方面具有重要意义。通过研究高光谱遥感影像光谱特征，结合信息熵理论，应用高斯分布函数，建立了一种新的高光谱影像端元提取算法，即光谱最小信息熵（spectral minimum shannon entropy，SMSE）算法。将该算法应用于AVRIRS高光谱影像的端元光谱提取，并经过与美国地质勘探局（United States Geological Survey， USGS）波谱库中的数据匹配，得知其提取端元的精度较高。同时，通过与经典的纯净像元指数（pixel purity index，PPI）和连续最大角凸锥（sequential maximum angle convex cone，SMACC）等端元提取算法进行实验比较和结果综合分析，发现光谱最小信息熵算法提取端元光谱效率更高、精度更好。此外，分别利用SMACC和SMSE提取 Hyperion高光谱影像端元，得出SMSE的端元提取效果好于SMACC，从而可认为SMSE 算法具有一定普适性。
단원제취시혼합상원분해적관건，연구기산법재고정도적지물식별、봉도반연화정량요감등방면구유중요의의。통과연구고광보요감영상광보특정，결합신식적이론，응용고사분포함수，건립료일충신적고광보영상단원제취산법，즉광보최소신식적（spectral minimum shannon entropy，SMSE）산법。장해산법응용우AVRIRS고광보영상적단원광보제취，병경과여미국지질감탐국（United States Geological Survey， USGS）파보고중적수거필배，득지기제취단원적정도교고。동시，통과여경전적순정상원지수（pixel purity index，PPI）화련속최대각철추（sequential maximum angle convex cone，SMACC）등단원제취산법진행실험비교화결과종합분석，발현광보최소신식적산법제취단원광보효솔경고、정도경호。차외，분별이용SMACC화SMSE제취 Hyperion고광보영상단원，득출SMSE적단원제취효과호우SMACC，종이가인위SMSE 산법구유일정보괄성。
It’s significant to study the algorithm of endmember extraction,which is the key for pixel unmixing,in the fields of feature identification,abundance inversion,quantitative remote sensing and so on.Based on the theory of shannon entropy and Gaussian distribution function,a new algorithm,named spectral minimum shannon entropy (SMSE)method for extracting end-members of hyperspectral images,is proposed in the present paper after analyzing the characteristics of spectra of the hyperspec-tral images.This algorithm was applied to extract the endmembers of an AVRIRS hyperspectral image,it was found that these extracted endmember spectra have higher precision by matching with the spectral library of United States Geological Survey (USGS).At the same time,it was also found that the SMSE algorithm has better efficiency and accuracy for extracting end-member spectra through comparing and analyzing comprehensively the results of endmember extraction of the experimental data by using the methods of SMSE,pixel purity index(PPI),sequential maximum angle convex cone(SMACC)and so on.In addi-tion,the SMACC and SMSE are used to extract the endmembers in a Hyperion hyperspectral image,and it is concluded that the results of the SMSE is better than the SMACC’s.Thus,the SMSE algorithm can be thought to have a certain degree of univer-sal applicability.