Fast Spectral Embedded Clustering Based on Structured Graph Learning for Large-Scale Hyperspectral Image

Hyperspectral image (HSI) contains rich spectral information and spatial features, but the huge amount of data often leads to problems of low clustering accuracy and large computational complexity. In this letter, a new clustering method for HSI is proposed, which is named fast spectral embedded clustering based on structured graph learning (FSECSGL). First, the low-dimensional representation of data can be obtained to reduce the scale by the fast spectral embedded method. Then, we use the embedded data to learn an optimal similarity matrix by structured graph learning. Furthermore, the learning structure graph gives feedback to the original bipartite graph to generate better spectral embedded data. As a result, we can obtain a better similarity matrix and clustering result by iteration, which can overcome the limitation of -means initialization. Experiments show that this method can obtain good clustering performance compared with other methods.

Automated summary

This letter proposes a new clustering method named fast spectral embedded clustering based on structured graph learning (FSECSGL) for hyperspectral image (HSI) analysis. By reducing data dimension and learning an optimal similarity matrix, this method achieves improved clustering performance.