Spatial Sparse subspace clustering for Compressive Spectral imaging

TL;DR

This paper introduces a novel spectral image clustering method directly from CASSI compressive measurements, combining sparse subspace clustering with spatial regularization to improve accuracy using optimal coded apertures.

Contribution

It develops a new clustering approach that integrates spatial regularization into sparse subspace clustering for spectral images from compressive measurements, enhancing performance.

Findings

Improved clustering accuracy demonstrated on real data.

Effective use of spatial information in spectral image clustering.

Optimal coded apertures enhance measurement quality.

Abstract

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.