J
Jocelyn Chanussot
Researcher at University of Grenoble
Publications - 703
Citations - 39402
Jocelyn Chanussot is an academic researcher from University of Grenoble. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 73, co-authored 614 publications receiving 27949 citations. Previous affiliations of Jocelyn Chanussot include German Aerospace Center & University of Savoy.
Papers
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Detail Injection-Based Deep Convolutional Neural Networks for Pansharpening
TL;DR: This article developed a DCNN that outperforms both the other detail injection-based proposals and several state-of-the-art pansharpening methods and is inspired by the direct difference between the PAN image and the upsampled low-resolution MS image.
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Urban Mapping Using Coarse SAR and Optical Data: Outcome of the 2007 GRSS Data Fusion Contest
TL;DR: The 2007 data fusion contest was dealing with the extraction of a land use/land cover maps in and around an urban area, exploiting multitemporal and multisource coarse-resolution data sets, and the best algorithm is based on a neural classification enhanced by preprocessing and postprocessing steps.
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Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)
Behnood Rasti,Danfeng Hong,Renlong Hang,Pedram Ghamisi,Xudong Kang,Jocelyn Chanussot,Jon Atli Benediktsson +6 more
TL;DR: In this paper, the authors provide a technical overview of the state-of-the-art feature extraction approaches for hyperspectral images, providing useful entry points for researchers at different levels, including students, researchers, and senior researchers willing to explore novel investigations on this challenging topic.
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Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model.
Danfeng Hong,Jingliang Hu,Jing Yao,Jocelyn Chanussot,Jocelyn Chanussot,Xiao Xiang Zhu,Xiao Xiang Zhu +6 more
TL;DR: Extensive experiments conducted demonstrate the superiority and advancement of the S2FL model in the task of land cover classification in comparison with previously-proposed state-of-the-art baselines.
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Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture
TL;DR: An overview on the use of airborne multispectral and hyperspectral imagery and high-resolution satellite imagery for assessing crop growth and yield variability and the methodologies for image acquisition and processing and for the integration and analysis of image and yield data are discussed.