1 research outputs found
Spatial Sparse subspace clustering for Compressive Spectral imaging
This paper aims at developing a clustering approach with spectral images
directly from CASSI compressive measurements. The proposed clustering method
first assumes that compressed measurements lie in the union of multiple
low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an
unsupervised method that assigns compressed measurements to their respective
subspaces. In addition, a 3D spatial regularizer is added into the SSC problem,
thus taking full advantages of the spatial information contained in spectral
images. The performance of the proposed spectral image clustering approach is
improved by taking optimal CASSI measurements obtained when optimal coded
apertures are used in CASSI system. Simulation with one real dataset
illustrates the accuracy of the proposed spectral image clustering approach