Normalized Cut与分水岭变换在高光谱影像混合像元端元提取中的应用

Endmemeber extraction for hyperspectral image based on normalized cut and watershed transformation

现有的遥感影像端元提取方法主要是从光谱特征角度提出,而结合空间信息的端元提取方法是近些年遥感影像混合像元分解的研究热点,为此使用图论的图像分割Normalized Cut与分水岭变换方法提出了一种改进的空间预处理模型用于高光谱遥感影像混合像元的端元提取。该方法在混合像元端元提取过程中不仅利用遥感影像的光谱信息而且引入了像元的空间位置信息,实验结果表明本文提出的端元提取方法与现有的方法相比提高了遥感影像的混合像元分解精度。

As spatial information plays an important role in remote sensing analysis, more and more researchers pay focus on spectral-spatial endmember extraction. An improved endmember extraction method with a spatial preproeessing module, which uses watershed with normalized cuts to avoid over-segmentation and producing accurate results from spectral mixture analysis, is proposed in this paper. According to the experiment in this study the spatial-spectral endmemher extraction method can generate a more accurate pixel un-mixing results during image segmentation.