An Amalgamate Classification Approach on Hyperspectral Imaging

Abstract

The collection and processing of data across the electromagnetic spectrum are called hyperspectral imaging. The main aim of the hyperspectral imaging is to get the spectrum for every pixel in the scene of the image and it is used to find the objects, finding materials and also for detecting the process. Many types of research have been done on hyperspectral imaging is done for the detection of various types of spectral images. In this paper, An Amalgamate Classification Approach (ACA) is introduced to find the various types of data with improved accuracy is shown in this system. The dataset used is synthetic images that are used to find the greenery in the given images