Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier

Abstract

Hyperspectral remote sensing inversion models utilize spectral information over optically shallow waters to retrieve optical properties of the water column, bottom depth and reflectance, with the latter used in benthic classification. Accuracy of these retrievals is dependent on the spectral endmember(s) used to model the bottom reflectance during the inversion. Without prior knowledge of these endmember(s) current approaches must iterate through a list of endmember-a computationally demanding task. To address this, a novel lookup table classification approach termed HOPE-LUT was developed for selecting the likely benthic endmembers of any hyperspectral image pixel. HOPE-LUT classifies a pixel as sand, mixture or non-sand, then the latter two are resolved into the three most likely classes. Optimization subsequently selects the class (out of the three) that generated the best fit to the remote sensing reflectance. For a coral reef case, modeling results indicate very high benthic classification accuracy (>90%) for depths less than 4 m of common coral reef benthos. These accuracies decrease substantially with increasing depth due to the loss of bottom information, especially the spectral signatures. We applied this technique to hyperspectral airborne imagery of Heron Reef, Great Barrier Reef and generated benthic habitat maps with higher classification accuracy compared to standard inversion models.

Water Column Optical Properties of Pacific Coral Reefs Across Geomorphic Zones and in Comparison to Offshore Waters

“…Terrestrially sourced particulates such as river input or run-off can increase particle load, limiting PAR and smothering or physically burying corals [37,38]. In addition to defining the light regimes under which coral reefs exist, the optical properties of the water column must be accounted for in remote sensing studies of coral reefs and other benthic constituents [8,[52][53][54][55][56]. Many remote sensing studies presume that reef waters are clear and that the optical properties of oligotrophic offshore waters can be used as a proxy for reef systems [57].…”

Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

“…Since the spectral shape of backscattering is fairly flat, incorporating high scattering in the seafloor rather than water column does not significantly influence the retrieval of the spectrally varying constituents such as bottom type and absorption by phytoplankton nor influences retrievals of bathymetry. The HOPE model used here was parameterized for seagrass and sediment, but other benthic constituents could be added to provide additional information on other submerged aquatic vegetation such as the green and red macroalgae that occurred along the shore and mixtures of bottom types [59]. Various different permutations of semi-analytical inversion models have been evaluated with different numbers of bottom types, and analytical solutions have been developed in recent years [12,59,75].…”

“…The HOPE model used here was parameterized for seagrass and sediment, but other benthic constituents could be added to provide additional information on other submerged aquatic vegetation such as the green and red macroalgae that occurred along the shore and mixtures of bottom types [59]. Various different permutations of semi-analytical inversion models have been evaluated with different numbers of bottom types, and analytical solutions have been developed in recent years [12,59,75]. Comparing such approaches widely across different ecosystems would be an important next step.…”

“…Here, we refine the parameterization of B as a pure endmember of a selected bottom type in the HOPE model and treat R b as a linear, or areal, mixture based on a library of known endmembers. Such linear unmixing has also been incorporated by other researchers using the HOPE model (e.g., [57][58][59]). These endmembers can be measured in situ or drawn from a suitable spectral library.…”

An Automatic Sparse Pruning Endmember Extraction Algorithm with a Combined Minimum Volume and Deviation Constraint

“…Much research has been under taken on remotely sensed hyperspectral data processing in recent years, covering topics such as dimensionality reduction [1][2][3], classification [4,5], target detection [6][7][8], data compression [9,10], and spectral unmixing [11][12][13][14][15][16][17][18][19]. Most of this research assumes that each pixel vector comprises the response of a single underlying material in the scene.…”