Efficient subspace clustering of hyperspectral images using similarity-constrained sampling

The unsupervised classification of hyperspectral images (HSIs) draws attention in the remote sensing community due to its inherent complexity and the lack of labeled data. Among unsupervised methods, sparse subspace clustering (SSC) achieves high clustering accuracy by constructing a sparse affinity matrix. However, SSC has limitations when clustering HSI images due to the number of spectral pixels. Specifically, the temporal complexity grows at a cubic ratio of the size of the data, making it inefficient for addressing HSI subspace clustering. We propose an efficient SSC-based method that significantly reduces the temporal and spatial computational complexity by splitting the HSI clustering task using similarity-constrained sampling. Our similarity-constrained sampling strategy considers both edge and superpixel information of the HSI to boost the clustering performance. This sampling strategy enables an intelligent selection of spectral signatures, and then, we split the clustering problem into multiples threads. Experimental results on widely used HSI datasets show that the efficiency of the proposed method outperforms baseline methods by up to 30% in overall accuracy and up to six times in computing time. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

Automated summary

The unsupervised classification of hyperspectral images using sparse subspace clustering (SSC) faces limitations due to the number of spectral pixels, but this study proposes an efficient SSC-based method that reduces computational complexity through similarity-constrained sampling. Experimental results demonstrate that this method outperforms baseline methods with up to 30% higher accuracy and up to six times faster computing time.