Prof. Ben Somers Profile

Prof. Ben Somers

Professor, Applied Ecology & Remote Sensing at KU Leuven

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Publications (6)

Proceedings Article | 12 September 2021 Presentation + Paper

Deep learning for sub-pixel palm tree classification using spaceborne Sentinel-2 imagery

María Culman, Andrés Rodríguez, Jan Dirk Wegner, Stephanie Delalieux, Ben Somers

Proceedings Volume 11856, 118560E (2021) https://doi.org/10.1117/12.2599861

KEYWORDS: RGB color model, Remote sensing, Image resolution, Image segmentation, Vegetation, Image classification, Satellites, Orthophoto maps

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Proceedings Article | 26 October 2016 Presentation + Paper

Spectral unmixing of urban land cover using a generic library approach

Jeroen Degerickx, Marian-Daniel Lordache, Akpona Okujeni, Martin Hermy, Sebastian van der Linden, Ben Somers

Proceedings Volume 10008, 100080L (2016) https://doi.org/10.1117/12.2241189

KEYWORDS: Library classification systems, Sensors, Hyperspectral imaging, Vegetation, Expectation maximization algorithms, Computer simulations, Image processing, Hyperspectral simulation, Image classification, Multiplexers

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Proceedings Article | 21 October 2014 Paper

Vegetation index correction to reduce background effects in orchards with high spatial resolution imagery

Jonathan Van Beek, Laurent Tits, Ben Somers, Tom Deckers, Pieter Janssens, Pol Coppin

Proceedings Volume 9239, 923910 (2014) https://doi.org/10.1117/12.2067182

KEYWORDS: Vegetation, Spatial resolution, Satellites, Biological research, Remote sensing, Sensors, Soil science, Earth observing sensors, Agriculture, Reflectivity

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Proceedings Article | 17 October 2013 Paper

Improving the efficiency of MESMA through geometric unmixing principles

Laurent Tits, Ben Somers, Rob Heylen, Paul Scheunders, Pol Coppin

Proceedings Volume 8892, 88920Q (2013) https://doi.org/10.1117/12.2028844

KEYWORDS: Signal to noise ratio, Shape memory alloys, Reflectivity, Image processing, Computer simulations, Soil science, Neodymium, Signal processing, Vegetation, Remote sensing

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Proceedings Article | 16 October 2013 Paper

Alternating least-squares unmixing for the extraction of sub-pixel information from agricultural areas

Laurent Tits, Ben Somers, Wouter Saeys, Pol Coppin

Proceedings Volume 8887, 888706 (2013) https://doi.org/10.1117/12.2028851

KEYWORDS: Vegetation, Remote sensing, Chemical analysis, Agriculture, Chemometrics, Hyperspectral imaging, Absorption, Statistical analysis, Expectation maximization algorithms, Soil science

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Alternating Least Squares (ALS) is a blind source separation method commonly used in Chemometrics to simultaneously estimate the absorption spectrum and concentration of the different components in a chemical sample. In this study, the transferability of ALS from Chemometrics to agricultural Remote Sensing is evaluated. Due to the subpixel contribution of background components, spectral unmixing has become an indispensable processing step in the spectral analysis of agricultural areas. Yet, traditional unmixing techniques only allow estimating the sub-pixel cover distribution of the different components, but fail to provide an estimate of the pure spectral signature of the crop. This info is, however, highly valuable as this pure crop signature could be used to monitor the health status of the trees. Here, we anticipate that ALS can provide a solution. ALS estimates both the concentration and the absorption spectra of the different components in a chemical sample and this can easily be translated into estimating both the subpixel cover fraction and spectral signature of the different components in a mixed image pixel. The ALS model was tested on simulated hyperspectral images of Citrus orchards in which ray-tracing software was used to realistically incorporate spectral variability, multiple scattering and shadowing effects. Both the accuracy of the extracted cover fractions and the pure spectral signatures of the crop were assessed, as well as the accuracy with which the biophysical parameters of the trees (i.e. chlorophyll content, leaf water content and Leaf Area Index) could be derived from the extracted crop signature. ALS indeed allowed to simultaneously estimate the subpixel cover distribution (RMSE = 0.05), as well as the pure spectral signatures of the different endmembers (RRMSE < 0.12), and considerably improved the extraction of biophysical parameters (ΔR2 up to 0.43). ALS thus provides a promising new image analysis tool for agricultural remote sensing.

Proceedings Article | 16 October 2013 Paper

Mapping tropical rainforest canopies using multi-temporal spaceborne imaging spectroscopy

Ben Somers, Gregory Asner

Proceedings Volume 8887, 888704 (2013) https://doi.org/10.1117/12.2028508

KEYWORDS: Imaging spectroscopy, Feature selection, Reflectivity, Remote sensing, Hyperspectral simulation, Sensors, Vegetation, Signal to noise ratio, Hyperspectral imaging, Ecosystems

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