Hyperspectral Image Classification Framework via Information Fusion and Bilateral Filtering

In recent years, different from the previous hyperspectral image (HSI) classification method which only considers spectral information or spatial information, people gradually realize that information in different fields is equally important. Therefore, this paper proposes a hyperspectral image classification method called IFBF based on information fusion and bilateral filtering. The proposed IFBF method includes the following main steps. Firstly, morphological operation and super pixel segmentation are respectively used to extract the pixel-level and super pixel-level feature information of HSI. Secondly, two different levels of information are given different weights, and they are fused by decision fusion. Then, considering that multi-level information fusion will bring information redundancy, bilateral filtering method is used to filter the fused image, and finally LDM classification method is used for classification. Experimental results on the real dataset show better performance than several well-known classifications methods.