Spectral image clustering

Traditional spectral imaging sensors acquire high-dimensional data that are used for the discrimination of objects and features in a scenes. Recently, a new architecture known as the coded aperture snapshot spectral imager (CASSI) has been presented for the acquisition of compressive spectral image data with just a few coded focal plane array measurements. This paper focus on developing a clustering approach with spectral images directly from CASSI compressive measurements, without reconstructing the full data cube. 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 the optical system. Motivated by an emerging field of compressed subspace clustering for dimensionality reduction while keeping the distance, the set of optimal coded apertures is designed such that the CASSI sensing matrix can be used for the compression of two subspaces based on the generalized projection $ F $-norm distance. Simulation with two real datasets illustrates the accuracy of the proposed spectral image clustering approach.