目前成熟的端元提取算法是基于单形体几何学的像元纯度指数(PPI)算法, N-FINDR, VCA等算法。 这些算法从图像所有像元中提取纯光谱, 具有提取速度慢、 精度不高的缺点； 部分算法需要进行光谱降维, 不利于小目标信息的提取。 该文提出先利用基于空间特征的光谱分类算法进行分类, 将整个图像划分成空间相邻、 光谱相似的若干类, 每一类的均值光谱作为标准光谱, 从所有类别的标准光谱中提取纯光谱, 使得运算量明显减少, 并且降低了噪声对算法的影响, 极大的提高了端元提取的速度和精度。 同时采用基于光谱冗余的端元提取算法进行端元提取, 不需要预先设定端元数目, 相对于PPI, N-FINDR等算法, 该算法更具合理性。 将该算法处理结果与ENVI中的SMACC算法处理结果进行比较, 表明该算法具有端元提取准确, 空间连续性好, 抗噪能力强等特点。

Spectral unmixing is an important task for data processing of hyperspectral remote sensing, which is comprised of extracting the pure spectra (endmember) and calculating the abundance value of pure spectra. The most efficient endmember extracting algorithms (EEAs) is designed based on convexity geometry such as pure pixel index (PPI), N-finder algorithm (N-FINDR). Most EEAs choose pure spectra from all pixels of an image so that they have disadvantages like slow processing speed and poor precision. Partial algorithms need reducing the spectral dimension, which results in the difficulty in small target identification. This paper proposed an algorithm that classifies the hyperspetral image into some classes with homogeneous spectra and considers the mean spectra of a class as standard spectra for the class, then extracts pure spectrum from all standard spectra of classes. It reduces computation and the effect of system error, enhancing the speed and precision of endmember extraction. Using the least squares with constraints on spectral extraction and spectral unmixing, by controlling the band average value of the maximum spectral redundant allowance to control the number of endmembers, does not need to reduce the spectral dimension and predetermine the number of endmembers, so compared to N-finder algorithm, such algorithm is more rational.