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Monitoring of coastal areas by remote sensing is an important issue. The interest of using an unmixing method to determine the seabed composition from hyperspectral aerial images of coastal areas is investigated. Unmixing provides both seabed abundances and endmember reflectances. A sub-surface mixing model is presented, based on a recently proposed oceanic radiative transfer model that accounts for seabed adjacency effects in the water column. Two original non-negative matrix factorization (N MF)-based unmixing algorithms, referred to as WADJU M (Water ADJacency UnMixing) and WU M (Water UnMixing, no adjacency effects) are developed, assuming as known the water column bio-optical properties. Simulations show that WADJU M algorithm achieves performance close to that of the N MF-based unmixing of the seabed without any water column, up to 10 m depth. WU M performance is lower and decreases with the depth. The robustness of the algorithms when using erroneous information about the water column bio-optical properties is evaluated. The results show that the abundance estimation is more reliable using WADJU M approach. WADJU M is applied to real data acquired along the French coast; the derived abundance maps of the benthic habitats are discussed and compared to the maps obtained using a fixed spectral library and a least-square (LS) estimation of the seabed mixing coefficients. The results show the relevance of the WADJU M algorithm for the local analysis of the benthic habitats.

Mapping Benthic Habitats by Extending Non-Negative Matrix Factorization to Address the Water Column and Seabed Adjacency Effects