Spectral similarity‐based feature for hyperspectral image classification

Abstract

AbstractBased on spectral similarity measure, a unified spectral similarity‐based framework is developed to generate a new series of spectral similarity‐based features for hyperspectral image classification by using classifier, such as support vector machine(SVM). First, a reference spectral signature was defined according to the original hyperspectral data. The reference spectral signature can be chosen as the uniform line or the average of the original data. Second, the proposed features were produced by calculating the spectral similarity measures between each pixel's and the reference spectral signature. The experiment result shows that these proposed features can improve hyperspectral image classification accuracies.