Hyperspectral image classification: A benchmark

Hyperspectral image classification, an astonishing tool to distinguish the land covers in remote sensed hyperspectral images, has been investigated by multiple disciplines such as geoscience, environmental science, mathematics, and computer vision. Following early machine learning (e.g., support vector machines and neural networks) and feature extraction theories (e.g., principal component analysis), hundreds of hyperpsectral image classification algorithms have been proposed in order to further improve the classification accuracies. However, it is still unclear what are the real improvements of the newly proposed methods in this field or we are just fitting models to some specific data sets? To address this problem, this paper aims at discussing the major motivations and ideas in conducting a comprehensive benchmark analysis for hyperspectral image classification. The benchmark should not only allows researchers to compare their models with other algorithms but also helps identify the chief factors affecting the performance of their classification methods.