A Multi-Sensor Subspace-Based Clustering Algorithm Using RGB and Hyperspectral Data

In this work, we introduce a multi-sensor subspace-based clustering algorithm that benefits from fine spectral-resolution hyperspectral images (HSIs) and fine spatial-resolution RGB images. In order to extract spatial information, a hidden Markov random field (HMRF) is employed on the fine spatial-resolution RGB image, whereas, spectral information is derived from an HSI using an advanced sparse subspace clustering algorithm. The proposed algorithm is validated on two real geological data sets. The experimental results in this study show that the proposed algorithm outperforms the state-of-the-art clustering algorithms in terms of clustering accuracy.