基于新型光谱相似度量的高光谱影像谱聚类算法

Novel Spectral Similarity Measurement Based Spectral Clustering Algorithm in Hyperspectral Imagery

高斯径向基函数是基于光谱向量间欧氏距离的度量,其对于同种地物光谱变化的适应性较弱,使得基于高斯径向基函数的高光谱影像谱聚类算法的性能下降。为了解决该问题,从光谱曲线形状描述出发,基于光谱角度余弦提出了一种新型光谱相似度量,并将其用于构建谱聚类算法的亲和度矩阵。最后利用多组高光谱数据进行了实验分析,结果证明了该算法的有效性。

As the gaussian radial basis function（RBF） is based on the Euclidean distance of two spectral vectors,it is not sensitive for variation of spectral curves of a material,which results in decrease of the performance of the RBF based spectral clustering for hyperspectral imagery degenerate.In order to solve this problem,according to the spectral curves similarity description,a novel spectral similarity measurement based on spectral angle cosine was proposed,and the measurement was used to build the affinity matrix used by spectral clustering algorithms.Finally,the experiments carried on with several hyperspectral data.The results of the experiments prove the validity of the proposed method.