Joint estimation of water column parameters and seabed reflectance combining maximum likelihood and unmixing algorithm

Guillaume, Mireille; Michels, Yves; Jay, Sylvain

doi:10.1109/whispers.2015.8075496

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…In this study, regularised methods have been evaluated in homogeneous sandy habitats because, in its current version, ML (and therefore MAP) assumes that the water quality and bottom type are homogeneous within a local neighbourhood (i.e., the statistical sample). To deal with more complex bottom covers or lower spatial resolutions, the generalization of ML and MAP to heterogeneous bottoms is currently investigated and involves a modified likelihood function and an unmixing technique to provide a different estimate of bottom spectrum in each pixel (Guillaume, Michels, and Jay 2015). Note that the proposed regularisation approach can naturally be extended either to multispectral data (similarly to non-regularised methods) and to other similar estimation method in the same way.…”

Section: Discussionmentioning

confidence: 99%

Regularized estimation of bathymetry and water quality using hyperspectral remote sensing

Jay

Guillaume

2016

International Journal of Remote Sensing

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International audienceCoastal water mapping from remote-sensing hyperspectral data suffers from poor retrieval performance when the targeted parameters have little effect on subsurface reflectance, especially due to the ill-posed nature of the inversion problem. For example,depth cannot accurately be retrieved for deep water, where the bottom influence is negligible. Similarly, for very shallow water it isdifficult to estimate the water quality because the subsurface reflectance is affected more by the bottom than by optically activewater components. Most methods based on radiative transfer model inversion do not consider the distribution of targeted parameters within the inversion process, thereby implicitly assuming that any parameter value in the estimation range has the same probability. In order to improve the estimation accuracy for the above limiting cases, we propose to regularize the objective functions of two estimation methods (maximum likelihood or ML, and hyperspectral optimization process exemplar, or HOPE) by introducing local prior knowledge on the parameters of interest. To do so, loss functions are introduced into ML and HOPE objective functions in order to reduce the range of parameter estimation. These loss functions can be characterized either by using prior or expert knowledge, or by inferring this knowledge from the data (thus avoiding the use of additional information). This approach was tested both on simulated and real hyperspectral remote-sensing data. We show that the regularized objective functions are more peaked than their non-regularized counterparts when the parameter of interest has little effect on subsurface reflectance. As a result, the estimation accuracy of regularized methods is higher for these depth ranges. In particular, when evaluated on real data, these methods were able to estimate depths up to 20 m, while corresponding non-regularized methods were accurate only up to 13 m on average for the same data. This approach thus provides a solution to deal with such difficult estimation conditions. Furthermore, because no specific framework is needed, it can be extended to any estimation method that is based on iterative optimization

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Section: Discussionmentioning

confidence: 99%

Regularized estimation of bathymetry and water quality using hyperspectral remote sensing

Jay

Guillaume

2016

International Journal of Remote Sensing

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“…Some existing algorithms can tackle this issue with a high level of accuracy [42][43][44]. When unmixing pixels, algorithms may face errors due to the heterogeneity of seabed reflectance, disturbing the radiance with the light scattered on the neighboring elements [45]. This process, called the adjacency effect, has negative effects on the accuracy of remote sensing [46] and can modify the radiance by up to 26% depending on turbidity and water depth [47].…”

Section: Spatial and Spectral Resolutionsmentioning

confidence: 99%

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

et al. 2021

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Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

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“…(i) The linear mixing model is currently used for seabed estimation with other methods (such as LS inversion with a spectral library) [9,18,20,21,33,[35][36][37][38]46].…”

Section: Decomposition Of Target and Environment Reflectancesmentioning

confidence: 99%

“…Seabed mapping is also developed in [36]. In [37], Maritorena's radiative transfer model [13] has been used to combine iteratively ML estimation and N MF unmixing to obtain simultaneously the water parameters, the endmember spectra and the relative abundances. Recently a Bayesian approach has been proposed [20] for linear mixtures to jointly estimate the seabed reflectance and water optical properties while flexibly incorporating varied domain knowledge and in situ measurements, using Maritorena's model, and simulations from Hydrolight radiative transfer code.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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 M F )-based unmixing algorithms, referred to as W A D J U M (Water ADJacency UnMixing) and W U M (Water UnMixing, no adjacency effects) are developed, assuming as known the water column bio-optical properties. Simulations show that W A D J U M algorithm achieves performance close to that of the N M F -based unmixing of the seabed without any water column, up to 10 m depth. W U 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 W A D J U M approach. W A D J U 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 ( L S ) estimation of the seabed mixing coefficients. The results show the relevance of the W A D J U M algorithm for the local analysis of the benthic habitats.

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Joint estimation of water column parameters and seabed reflectance combining maximum likelihood and unmixing algorithm

Cited by 5 publications

References 6 publications

Regularized estimation of bathymetry and water quality using hyperspectral remote sensing

Regularized estimation of bathymetry and water quality using hyperspectral remote sensing

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

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

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