Dr. Yuliya Tarabalka Profile

Dr. Yuliya Tarabalka

SPIE Involvement:

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Proceedings Article | 9 October 2018 Presentation + Paper

Digital surface model generation from multiple optical high-resolution satellite images

Lionel Mattéo, Yuliya Tarabalka, Isabelle Manighetti

Proceedings Volume 10789, 107890W (2018) https://doi.org/10.1117/12.2325135

KEYWORDS: Satellites, Satellite imaging, Earth observing sensors, Sensors, Sun, Spatial resolution, Data acquisition, Image resolution, Image acquisition

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The modern optical satellite sensors capture images in stereo and tri-stereo acquisition modes. This allows reconstruction of high-resolution (30-70 cm) topography from the satellite data. However, numerous areas on the Earth exhibit complex topography with a lot of “discontinuities”. One case is tectonic fault sites, which form steep topographic escarpments enclosing narrow, deep corridors that mask parts of the ground. Built with common approaches (stereo or tri-stereo), a digital surface model (DSM) would not recover the topography in these masked zones. In this work, we have settled on a new methodology, based on the combination of multiple satellite Pleiades images taken with different geometries of acquisition (pitch and roll angles), with the purpose to generate fully-resolved DSMs at very high-resolution (50 cm). We have explored which configurations of satellites (i.e., number of images and ranges of pitch and roll angles) allow to best measure the topography inside deep and narrow canyons. We have collected seventeen Pleiades Images with different configurations over the Valley of Fire fault zone, USA, where the fault topography is complex. We have also measured sixteen ground control points (GCPs) in the zone. From all possible combinations of 2 to 17 Pleiades images, we have selected 150 combinations and have generated the corresponding DSMs. The calculations are done by solving an energy minimization problem that searches for a disparity map minimizing the energy, which depends on the likelihood for pixels to belong to a unique point in 3D as well as regularization terms. We have statistically studied which combinations of images deliver DSMs with the best surface coverage, as well as the lowest uncertainties on geolocalisation and elevation measures, by using the GCPs. Our first results suggest that an exceeding time between our acquisitions leads to DSM with a low covered area. We conclude that Stereo and Tri-Stereo acquisition in one-single pass of the satellite will systematically generate a better DSM than multidate acquisition. We also conclude that in some cases, multi-date acquisitions with 7-8 images can improve the DSM robustness compared to multi-date acquisitions with fewer images.

Proceedings Article | 28 September 2009 Paper

Spectral-spatial analysis in hyperspectral remote sensing: from morphological profiles to classified segmentation

Jocelyn Chanussot, Jon-Atli Benediktsson, Mathieu Fauvel, Yuliya Tarabalka

Proceedings Volume 7477, 74770E (2009) https://doi.org/10.1117/12.836023

KEYWORDS: Image segmentation, Image classification, Hyperspectral imaging, Remote sensing, Expectation maximization algorithms, Composites, Mathematical morphology, Image processing algorithms and systems, Radon, Data fusion

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Conference Committee Involvement (11)

High-Performance Computing in Geoscience and Remote Sensing

13 September 2018 | Berlin, Germany

Image and Signal Processing for Remote Sensing

10 September 2018 | Berlin, Germany

High-Performance Computing in Geoscience and Remote Sensing

12 September 2017 | Warsaw, Poland

High-Performance Computing in Geoscience and Remote Sensing

28 September 2016 | Edinburgh, United Kingdom

High-Performance Computing in Remote Sensing

21 September 2015 | Toulouse, France

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