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

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 preprocessing 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 endmember extraction method can generate a more accurate pixel un-mixing results during image segmentation.