Hyperspectral unmixingwith sparse group lasso
Sparse unmixing has been recently introduced as a mechanism to characterize mixed pixels in remotely sensed hyperspectral images. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In available spectral libraries, it is observed that the spectral signatures appear organized in groups (e.g. different alterations of a single mineral in the U.S. Geological Survey spectral library). In this paper, we explore the potential of the sparse group lasso technique in solving hyperspectral unmixing problems. Our introspection in this work is that, when the spectral signatures appear in groups, this technique has the potential to yield better results than the standard sparse regression approach. Experimental results with both synthetic and real hyperspectral data are given to investigate this issue.