Boosting With Multiple Clustering Memberships For Hyperspectral Image Classification

A novel hyperspectral image classification algorithm is proposed and demonstrated on benchmark hyperspectral images. We also introduce a hyperspectral sky imaging dataset that we are collecting for detecting the amount and type of cloudiness. The algorithm is designed to be applied to the Such systems could improve the spatial and temporal resolution of cloud information vital to understanding Earth’s climate. We discuss the nature of our HSI-Cloud dataset being collected and an algorithm we propose for processing the dataset using a categorical-boosting method. The proposed method utilizes multiple clusterings to augment the dataset and achieves higher pixel classification accuracy. Creating categorical features via clustering enriches the data representation and improves boosting ensembles. For the experimental datasets used in this paper, gradient boosting methods performed favorably to the benchmark algorithms.