Classification of Hyperspectral imagery Using SIFT for Spectral Matching

Abstract

Hyperspectral remote sensing, as a new remote sensing technology, provides a powerful measure to object identification and classification accurately due to its acuminous ability of spectrum detection. This paper addresses the problem of the classification of hyperspectral remote-sensing images, and studies a novel spectral matching method based on the popular Scale-invariant Feature Transform (SIFT) technique. SIFT is a new method for extracting distinctive invariant features from images that can be perform reliable matching between different views of an object. In this method, spectral curves are transformed with SIFT, the SIFT features are extracted and taken as the comparing features for spectral matching and the minimum distance classifier is used to classify ground-objects. The experimental analysis has been carried out by using hyperspectral image acquired by the AVIRIS sensor on the Washington DC Mall. The obtained results confirm the effectiveness of SIFT in hyperspectral image classification with respect to conventional classifications.