基于波段影像统计信息量加权K-means聚类的高光谱影像分类

针对分类过程中如何合理利用高光谱影像波段问题,提出一种基于波段影像统计量加权K-means聚类的高光谱影像分类算法.该算法的核心思想在于:由波段含有的信息量及波段间的相关性确定各波段权重,同时考虑各波段对各聚类的重要性.首先,根据波段影像的熵、标准差及均值定义波段信息量函数,根据相邻波段影像互信息定义相关性函数;其次,...

Algorithm based on band statistical information weighted K-means for hyperspectral image classification

Aiming at the problem of how to use band information reasonably in the hyperspectral image classification, this paper proposes a hyperspectral image classification algorithm based on band statistical information weighted K-means clustering. The algorithm considers not only the quantity of information contained in each band and the correlation between bands but also the importance of each band to different clusters. The band weight is determined by the statistics of information and correlation functions. The statistics of information function is defined according to the entropy, standard deviation and mean value of the band image. The correlation function is defined according to the mutual information of adjacent band images. In order to express the importance of each band to different clusters, the band-category weight is introduced, and its entropy information measurement is defined. The above two types of weights are embedded into the K-means objective function. The final classification result can be obtained by minimizing the objective function. Classification experiments are conducted on Salinas and Pavia Centre hyperspectral images using the proposed algorithm, the traditional K-means algorithm, the PCA + K-means algorithm and the subspace band selection + K-means algorithm, respectively. The results demonstrate that the proposed algorithm is higher than the other three algorithms on overall accuracy and Kappa. It shows that the proposed algorithm can effectively improve the performance of hyperspectral image classification.