Iterative Edge Preserving Filtering Approach to Hyperspectral Image Classification

Abstract

This letter extends one of popular spectral-spatial classification methods for hyperspectral images, called edge preserving filtering (EPF)-based method to an iterative version of EPF method, referred to as iterative EPF (IEPF). Instead of finding maximum of the final soft probability maps obtained from the initial binary probability maps by EPF, the proposed IEPF feeds back the soft probability maps and combines them with the currently being processed image cube to create a new image cube as the next input to IEPF to reimplement support vector machine (SVM) for classification. The process is carried out iteratively by repeatedly feeding back the spatial information provided by EPF-obtained soft probability maps and terminated by a Tanimoto index (TI)-based automatic stopping rule. The experimental results demonstrate that IEPF performed better than EPF by providing higher classification accuracy.