Provided herein are algorithms and processes to extract endmembers from hyperspectral image data in real time. A Simplex Growing Algorithm is effective to estimate a p number of endmembers to be generated, to select one or more initial endmembers as a simplex of k members and to add a k+1 endmember to the simplex that yields a maximum simplex volume until k=p, thereby extracting one or more endmembers from the data. Alternatively, N-FINDR algorithms form an initial simplex set of p endmembers obtained from the hyperspectral image data, find a maximum volume of one or more initial p endmembers therewithin, replace one or more of the p endmembers within the simplex with one or more of the found p endmembers of maximum volume, and refind a maximum volume of p endmember(s) and replace p endmember(s) until no increase in p endmember(s) volume is found.
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机译：本文提供了实时地从高光谱图像数据中提取端成员的算法和过程。单形增长算法可有效地估计要生成的ap个末端成员，选择一个或多个初始末端成员作为k个成员的单纯形，并将k + 1个末端成员添加到该单纯形中，从而产生最大的单纯形体积，直到k = p ，从而从数据中提取一个或多个最终成员。替代地，N-FINDR算法形成从高光谱图像数据获得的p个末端成员的初始单纯形集合，在其中找到一个或多个初始p个末端成员的最大体积，用其中的一个或多个替换单个或多个p个末端成员中的一个或多个。找到找到的最大体积的p个端构件，并重新找到p个端构件的最大体积，并替换p个端构件，直到找不到p个端构件的体积增加为止。
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