A Dynamic Unmixing Framework for Plant Production System Monitoring
Hyperspectral remote sensing or imaging spectroscopy is an emerging technology in plant production monitoring and management. The continuous reflectance spectra allow for the intensive monitoring of biophysical and biochemical tree characteristics during growth, through for instance the use of vegetation indices. Yet, since most of the pixels in hyperspectral images are mixed, the evaluation of the actual vegetation state on the ground directly from the measured spectra is degraded by the presence of other endmembers, such as soil. Spectral unmixing, then, becomes a necessary processing step to improve the interpretation of vegetation indices. In this sense, an active research direction is based on the use of large collections of pure spectra, called spectral libraries or dictionaries, which model a wide variety of possible states of the endmembers of interest on the ground, i.e., vegetation and soil. Under the linear mixing model (LMM), the observed spectra are assumed to be linear combinations of spectra from the available dictionary. Combinatorial techniques (e.g., MESMA) and sparse regression algorithms (e.g., SUnSAL) are widely used to tackle the unmixing problem in this case. However, both combinatorial and sparse techniques benefit from appropriate library reduction strategies. In this paper, we develop a new efficient method for library reduction (or dictionary pruning), which exploits the fact that hyperspectral data generally lives in a lower-dimensional subspace. Specifically, we present a slight modification of the MUSIC-CSR algorithm, a two-step method which aims first at pruning the dictionary and second at infering high-quality reconstruction of the vegetation spectra on the ground (this application being called signal unmixing in remote sensing), using the pruned dictionary as input to available unmixing methods. Our goal is two-fold: 1) to obtain high-accuracy unmixing output using sparse unmixing, with low-execution time; and 2) to improve MESMA performances in terms of accuracy. Our experiments, which have been conducted in a multi-temporal case study, show that the method achieves these two goals and proposes sparse unmixing as a reliable and robust alternative to the combinatorial methods in plant production monitoring applications. We further demonstrate that the proposed methodology of combining a library pruning approach with spectral unmixing provides a solid framework for the year-round monitoring of plant production systems.