Detection and Classification of 2D and 3D Hyper Spectral Image using Enhanced Harris Corner Detector

Abstract

Image classification and visualization is a challenging task in hyper spectral imaging system. To overcome this
issue, here the proposed algorithm incorporates normalized correlation into active corner point of an image representation structure to perform hasty recognition by matching algorithm. Matching algorithms can be of two major categories, based on correlation and based on its features based on correlation and on its feature detection. Proposed algorithms often ignore issues related to scale and orientation and also those to be determined during the localization step. The task of localization involves finding the right region within the search image and passing this region to the verification process. A Harris corner detector is an advanced
approach to detect and extract a huge number of corner points in the input image. We integrate all the extracted corner points into a possible task to locate candidate regions in input image. In terms of detection and classification the proposed method has got better result.