The Fast Spectral Clustering Based on Spatial Information for Large Scale Hyperspectral Image

Spectral clustering is one of the most popular clustering approaches and has been applied in Hyperspectral Image (HSI) clustering well. However, most of these methods are not suitable for large scale HSI. In this paper, based on anchor graph and spatial information, we propose a novel method, called fast spectral clustering based on spatial information (FSCS), which could deal with large scale HSI and have better performance in user's accuracy, average accuracy, overall accuracy and so on. Firstly, based on the physical characteristic of HSI, FSCS algorithm combines the spatial information with spectral information, and uses the spatial nearest points to reconstructs the center point and reveal the local spatial structure. As a result, the correlation of pixels is strengthened and the clustering accuracy is improved. Secondly, the new adjacency matrix is constructed based on anchor graph and thus computational complexity is reduced significantly. Finally, in order to avoid tuning the heat-kernel parameter, the parameter-free strategy is adopted in FSCS. Experiments demonstrate the efficiency and effectiveness of the proposed FSCS algorithm.