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Quantitative Detection of Settled Dust Over Green Canopy Using Sparse Unmixing of Airborne Hyperspectral Data

The main task of environmental and geoscience applications is efficient and accurate quantitative classification of earth surfaces and spatial phenomena. In the past decade, there has been a significant interest in employing hyperspectral unmixing (HU) to retrieve accurate quantitative information latent in hyperspectral imagery data. Recently, the ground-truth and laboratory measured spectral signatures promoted by advanced algorithms are proposed as a new path toward solving the unmixing problem of hyperspectral imagery in semisupervised fashion. This paper suggests that the sensitivity of sparse unmixing techniques provides an ideal approach to extract and identify dust settled over/upon green vegetation canopy using hyperspectral airborne data. Among the available techniques, this study presents the results of seven selected algorithms: 1) non-negative matrix factorization (NMF); 2) L1 sparsity-constrained NMF (L1_NMF); 3) L1/2 sparsity-constrained NMF (L1/2_NMF); 4) graph regularized NMF (G_NMF); 5) structured sparse NMF (SS_NMF); 6) alternating least-square (ALS); and 7) Lin's projected gradient (LPG). The performance is evaluated on real hyperspectral imagery data via detailed experimental assessment. The results compared with performances of selected conventional unmixing techniques.