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A new maximum distance method based on barycentric coordinate for endmember extraction

机译:基于重心坐标进行终点坐标的新的最大距离方法

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In this paper, we present a new maximum distance method (MDM) by applying the simplex barycentric coordinate for endmember extraction, named BC-MDM. Under linear mixing model and pure-pixel assumption, the MDM, which is proposed by Geng in 2005, is based on the fact that a pixel is an endmember if it has the maximum distance to the sub-simplex with vertices being endmembers already extracted. Calculating the distance of a pixel to the sub-simplex is quite time-consuming. To overcome this problem, we introduce the idea of barycentric coordinate to MDM, which transforms the problem of distance calculation into spectral unmixing with abundance sum-to-one constraint. As a result, BC-MDM has greatly reduced the computational complexity of original MDM. The experimental results with real hyperspectral image demonstrate that compared to the simplex growing algorithm (SGA), the proposed method can provide the same performance with a computational complexity between one and two orders magnitude lower, and compared to vertex component analysis (VCA), our method can provide consistent result while requires less computing time.
机译:在本文中,我们通过应用EndMember提取的单纯形重心坐标来提出新的最大距离方法(MDM),命名为BC-MDM。在线性混合模型和纯像素假设下,2005年由Geng提出的MDM基于一个事实,即如果它具有与已经提取的顶点的顶点与子单位的最大距离,则像素是端部的事实。计算像素到子单位的距离非常耗时。为了克服这个问题,我们介绍了MDM的重心坐标的想法,它将距离计算问题转换为具有丰富的总和对一个约束的光谱解密。结果,BC-MDM大大降低了原始MDM的计算复杂性。实验结果与实际高光谱图像一起证明与单纯形生长算法(SGA)相比,所提出的方法可以提供相同的性能,在一个和两个订单幅度下,与顶点分量分析(VCA)相比,我们的计算复杂性方法可以提供一致的结果,而需要较少的计算时间。

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