Ms. Stephanie K. L. Delalieux Profile

Stephanie K. L. Delalieux

Researcher TAP at KU Leuven

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Publications (4)

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 | 17 October 2006 Paper

Stress detection in orchards with hyperspectral remote sensing data

P. Kempeneers, S. De Backer, P. Zarco-Tejada, S. Delalieux, G. Sepulcre-Cantó, F. Morales Iribas, J. van Aardt, P. Coppin, P. Scheunders

Proceedings Volume 6359, 635911 (2006) https://doi.org/10.1117/12.687842

KEYWORDS: Reflectivity, Sensors, Remote sensing, Data modeling, Optical filters, Iron, Vegetation, Biological research, Radiative transfer, Hyperspectral imaging

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Proceedings Article | 19 October 2005 Paper

Detection of natural and stress-induced variability in reflectance spectra of apple trees using hyperspectral analysis

Stephanie Delalieux, Wannes Keulemans, Jan van Aardt, Eddie Schrevens, Pol Coppin

Proceedings Volume 5976, 597611 (2005) https://doi.org/10.1117/12.627627

KEYWORDS: Reflectivity, Absorption, Data modeling, Vegetation, Resistance, Agriculture, Visible radiation, Photosynthesis, Spectral resolution, Statistical analysis

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Early detection of biotic and abiotic stresses and subsequent steering of agricultural systems using hyperspectral sensors potentially could contribute to the pro-active treatment of production-limiting factors. Venturia inaequalis (apple scab) is an important biotic factor that can reduce yield in apple orchards. Previous hyperspectral research focused on (i) determining if Venturia inaequalis leaf infections could be differentiated from healthy leaves and (ii) investigating at which developmental stage Venturia inaequalis infection could be detected. Logistical regression and partial least squares discriminant analysis were used to select the hyperspectral bands that best define differences among treatments. It was clear that hyperspectral data provide the contiguous, high spectral resolution data that are needed to detect subtle changes in reflectance values between healthy and stressed vegetation. The research was extended to include tree-based modeling as an alternative classification method. Results suggested that good predictability could be achieved when classifying infected plants based on this supervised classification technique. It was concluded that the spectral domain around 1600 nm was best suited to discriminate between infected and non-infected leaves immediately after infection, while the visible spectral region became more important at a well-developed infection stage. Research was focused on young leaves, because of the decreased incidence of infection in older leaves, the so-called 'ontogenic resistance'. Additional research was performed to gain a better understanding of the processes occurring during the first days after leaf unfolding and to evaluate the natural spectral variability among leaves. An undisturbed 20-day growth profile was examined to assess variations in the reflectance spectra due to physiological changes at the different growth stages of the leaves. Results suggested that an accurate distinction could be made between different leaf developmental stages using the 570 nm, 1940 nm, and 1460 nm wavelengths, and the red edge inflection point. Based on these results and the outcome of some existing chlorophyll indices, it was concluded that the chlorophyll content in leaves increased remarkably during the first 20 days after unfolding.

Proceedings Article | 26 October 2004 Paper

Upscaling of spectroradiometer data for stress detection in orchards with remote sensing

Pieter Kempeneers, Steve De Backer, Stephanie Delalieux, Sindy Sterckx, Walter Debruyn, Pol Coppin, Paul Scheunders

Proceedings Volume 5568, (2004) https://doi.org/10.1117/12.565523

KEYWORDS: Reflectivity, Atmospheric modeling, Sensors, Remote sensing, Vegetation, Atmospheric sensing, Radiative transfer, Transmittance, Nitrogen, Hyperspectral simulation

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