Emmanuel Baltsavias

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Combined usage and analysis of images from different sensors for various applications, including disaster monitoring, often needs first an image co-registration. Co-registration is based on automated matching of corresponding image features (e.g. just 10-40) and becomes very difficult when the images differ a lot. Perhaps the most difficult case is that of(More)
Hyperspectral cameras capture images with many narrow spectral channels, which densely sample the electromagnetic spectrum. The detailed spectral resolution is useful for many image analysis problems, but it comes at the cost of much lower spatial resolution. Hyperspectral super-resolution addresses this problem, by fusing a low-resolution hyperspectral(More)
Nowadays, different sensors and processing techniques provide Digital Elevation Models (DEMs) for the same site, which differ significantly with regard to their geometric characteristics and accuracy. Each DEM contains intrinsic errors due to the primary data acquisition technology, the processing chain, and the characteristics of the terrain. DEM fusion(More)
This paper presents a method to improve the robustness of automated measurements while also increasing the total amount of measured points and improving the point distribution. This is achieved by incorporating a tiling technique into existing automated interest point extraction and matching algorithms. The technique allows memory intensive interest point(More)
— The objective of this paper is to spatially predict tree/shrub genera using generalized linear models (GLM), color-infrared (CIR) aerial images, ADS40 images, digital surface models (DSM)s and field samples. The present study was carried out in the framework of the Swiss Mire Protection Program, where extraction of forest parameters for description of(More)
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