Elastic Graph Fusion Subspace Clustering for Large Hyperspectral Image
Yongshan Zhang, Xinxin Wang, Xinwei Jiang, Lefei Zhang, Bo Du
Abstract
Hyperspectral image (HSI) clustering is challenging to partition pixels into different clusters due to the complex spatial distribution and high-correlated spectrum. Subspace clustering is a representative learning paradigm and has shown competitive performance in HSIs. Most existing methods ignore potential spatial or structural information and show difficulties in dealing with large-scale HSIs. In this paper, we propose an elastic graph fusion subspace clustering (EGFSC) framework that can flexibly incorporate spectral, spatial and structural information for large HSI clustering. Instead of performing pixel-level learning, superpixel-level learning is conducted according to the generated superpixels to lessen computation burden and memory cost. To explore structural information in two perspectives, a superpixel graph and a band graph are constructed based on the superpixel features. Considering the incompatible sizes of the two graphs, we present three effective dual graph fusion strategies to fuse them in different ways. With these graph fusion strategies, EGFSC is able to improve clustering performance by simultaneously considering spatial and structural information. To solve the proposed framework, we present a closed-form solution for easy implementation. Experiments demonstrate that the proposed EGFSC obtains 70.08%, 75.76%, 87.28% and 77.23% clustering accuracies on the four HSI datasets and outperforms the state-of-the-art methods. The source code is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZhangYongshan/EGFSC</uri>.