Band selection of optimal for hyperspectral image fusion

A novel band selection model called joint skewness-kurtosis figure (JSKF) is proposed to solve problem of high dimensions of hyperspectral images. The whole data base is automatically partitioned into different sub-spaces by the sign and value of JSKF. Then the optimal bands for image fusion are selected in each sub-space according to the absolute value of JSKF. This band selection algorithm is applied in OMIS hyperspectral images and the selected bands are fused by a common image fusion method. The experimental results show that the bands selected by JSKF contain richer complementary information, especially the characteristics of small targets and textures, than those selected by the conventional adaptive band selection method and cumulative contribution rate method based on principal component analysis (PCA), and also provide improved fusion results.