Improved signal unmixing of vegetation using sparse group selection
Recently, signal unmixing was proposed in remote sensing with the goal to infer physical parameters of materials of interest, such as vegetation, on the ground. The typical approach uses large collections of pure spectra, called spectral libraries, in which many possible states of the vegetation are modeled by simulated or on-site acquired spectra. Spectra randomly selected from these libraries are used as input to dedicated unmixing methods, such as Multiple Endmember Spectral Mixture Analysis (MESMA). The spectra leading to the lowest reconstruction error are considered to be representative for the materials present in the pixel, such that the physical parameters of the ground vegetation can be inferred. However, the large number of spectra in the library imposes limits to the performance of this combinatorial approach, mainly related to running time constraints. In this paper, we propose the inclusion of a pre-processing step in the processing chain, based on the group lasso, which has the goal of selecting groups of signatures likely to be present in the mixtures. In this sense, the Group Sparse Unmixing via variable Splitting and Augmented Lagrangian (GSUnSAL) algorithm is used. The signatures contained in the groups selected by GSUnSAL are then used as input for MESMA. Our experiments using a real dataset acquired by an ASD spectrometer in a South-African orchard show that the proposed approach introduces important improvements in the signal unmixing solutions.