Classification of Hyper Spectral Images Using Hierarchical Guidance Filtering

Abstract

In this paper, we have proposed an ensemble system, that joins spectral and spatial data in various scales.  The proposed work, discusses two strategies to build the gathering model, to be specific, Hierarchical Guidance Filtering (HGF) and matrix of spectral angle distance (mSAD). HGF ,mSAD are consolidated by means of a weighted ensembling methodology. HGF is a various leveled edge-protecting sifting activity, which  creates differing test sets. In view of the yields of HGF, a progression of classifiers can be gotten. In this way, we characterize a low-position lattice, mSAD, to quantify the decent variety among preparing tests in every chain of importance. At last, a group technique is proposed utilizing the acquired individual classifiers and mSAD.