Spatial Sparse subspace clustering for Compressive Spectral imaging
This is an incremental improvement for spectral imaging applications, enhancing clustering performance in compressive sensing scenarios.
The paper tackles spectral image clustering directly from compressive CASSI measurements by proposing a spatial sparse subspace clustering method with a 3D spatial regularizer, achieving improved accuracy as demonstrated in simulations with a real dataset.
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.