Abstract:
Hyperspectral remote sensing images have a large number of spectral bands, which contribute to the fine classification and recognition of ground objects. However, with the band increasing, the data redundancy is raised correspondingly, making the image fusion computation complicated and the process complicated. Therefore, in this paper, combining the band background clarity, a wavelet weighted average method is proposed for hyperspectral image fusion. Taking the
J-M distance and the best index value as the index, the optimal band combination is extracted to reduce the band data redundancy and improve the information complementarily, which is propitious to hyperspectral image fusion. The algorithm contains the following three steps. Firstly, using
J-M distance and the principle of the best index selection, the optimal band and the preferred band combination are extracted from 115 bands of
HSI hyperspectral remote sensing images. Secondly, a single band remote sensing image background clarity processing EM algorithm for the remote sensing image are used to enhance the pretreatment of the selected band. Finally, the pixel level of wavelet weighted average is used to optimize the enhanced data of the band remote sensing image, which makes the quality of the fused image better. The experimental results show that this method improves the standard deviation, information content and clarity index of the fused image, which enhances the spatial detail and the surface features.