Hierarchical Clustering of Hyperspectral Images using Rank-Two Nonnegative Matrix Factorization
This work addresses clustering challenges in hyperspectral image analysis, which is important for remote sensing applications, but it is incremental as it builds on existing NMF techniques.
The paper tackles the problem of clustering high-resolution hyperspectral images by developing a hierarchical algorithm that uses rank-two nonnegative matrix factorization to split clusters, and it outperforms standard methods like k-means and NMF on synthetic and real-world datasets.
In this paper, we design a hierarchical clustering algorithm for high-resolution hyperspectral images. At the core of the algorithm, a new rank-two nonnegative matrix factorizations (NMF) algorithm is used to split the clusters, which is motivated by convex geometry concepts. The method starts with a single cluster containing all pixels, and, at each step, (i) selects a cluster in such a way that the error at the next step is minimized, and (ii) splits the selected cluster into two disjoint clusters using rank-two NMF in such a way that the clusters are well balanced and stable. The proposed method can also be used as an endmember extraction algorithm in the presence of pure pixels. The effectiveness of this approach is illustrated on several synthetic and real-world hyperspectral images, and shown to outperform standard clustering techniques such as k-means, spherical k-means and standard NMF.