Prof. Jocelyn Chanussot Profile

Prof. Jocelyn Chanussot

Professor

SPIE Involvement:

Conference Program Committee | Author

Websites:

Personal Website

Proceedings Article | 26 October 2022 Presentation + Paper

Evaluating the label efficiency of contrastive self-supervised learning for multi-resolution satellite imagery

Jules Bourcier, Gohar Dashyan, Jocelyn Chanussot, Karteek Alahari

Proceedings Volume 12267, 122670K (2022) https://doi.org/10.1117/12.2636350

KEYWORDS: Remote sensing, Satellites, Satellite imaging, Image classification, Classification systems, Machine learning, Computer vision technology, Optical imaging, Spatial resolution, High resolution satellite images

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SPIE Journal Paper | 25 October 2021 Open Access

Special Section Guest Editorial: Multitemporal Remote Sensing Data Processing and Applications

Liangpei Zhang, Jocelyn Chanussot, Assefa Melesse, Xinghua Li

JARS, Vol. 15, Issue 04, 042401, (October 2021) https://doi.org/10.1117/12.10.1117/1.JRS.15.042401

KEYWORDS: Remote sensing, Image classification, Data processing, Lithium, Vegetation, Synthetic aperture radar, Sensors, Landsat, LIDAR, Infrared sensors

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Proceedings Article | 12 July 2019 Open Access

Time-of-flight calibration of an MCT-APD sensor for a flash imaging LiDAR system

Victor E. Parahyba, Eric de Borniol, Regis Perrier, Yoanna-Reine Nowicki-Bringuier, Alessandra Ciapponi, Jocelyn Chanussot

Proceedings Volume 11180, 111802K (2019) https://doi.org/10.1117/12.2536011

KEYWORDS: Calibration, LIDAR, 3D metrology, Reflectivity, Sensors, Imaging systems, 3D acquisition, Avalanche photodetectors, 3D-TOF imaging, Staring arrays

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SPIE Journal Paper | 12 November 2015

Semiautomatic classification of cementitious materials using scanning electron microscope images

Lucas Drumetz, Mauro Mura, Samuel Meulenyzer, Sébastien Lombard, Jocelyn Chanussot

JEI, Vol. 24, Issue 06, 061109, (November 2015) https://doi.org/10.1117/12.10.1117/1.JEI.24.6.061109

KEYWORDS: Image segmentation, Cements, Image classification, Scanning electron microscopy, Image processing, X-rays, Electron microscopes, Image processing algorithms and systems, Denoising, Visualization

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Proceedings Article | 30 April 2015 Paper

Semi-automatic classification of cementitious materials using scanning electron microscope images

L. Drumetz, M. Dalla Mura, S. Meulenyzer, S. Lombard, J. Chanussot

Proceedings Volume 9534, 953403 (2015) https://doi.org/10.1117/12.2182762

KEYWORDS: Image segmentation, Cements, Denoising, Scanning electron microscopy, X-rays, X-ray imaging, Electron microscopes, Visualization, Stereolithography, Image classification

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Proceedings Article | 13 March 2013 Paper

Contextual filtering in curvelet domain for fluoroscopic sequences

Carole Amiot, Jérémie Pescatore, Jocelyn Chanussot, Michel Desvignes

Proceedings Volume 8669, 86690N (2013) https://doi.org/10.1117/12.2006795

KEYWORDS: Denoising, Digital filtering, Image processing, X-rays, X-ray imaging, Image filtering, Wavelets, Spatial filters, Medical imaging, Image quality

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Proceedings Article | 8 November 2012 Paper

Hierarchical watershed segmentation based on gradient image simplification

François Cokelaer, Mauro Dalla Mura, Jocelyn Chanussot

Proceedings Volume 8537, 85371O (2012) https://doi.org/10.1117/12.999358

KEYWORDS: Image segmentation, Image processing, Reconstruction algorithms, Remote sensing, Image filtering, Image processing algorithms and systems, Mathematical morphology, Chemical elements, Feature extraction, Filtering (signal processing)

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Proceedings Article | 8 November 2012 Paper

Detection of hedges based on attribute filters

Gabrielle Cavallaro, Benoit Arbelot, Mathieu Fauvel, Mauro Dalla Mura, Jón Atli Benediktsson, Lorenzo Bruzzone, Jocelyn Chanussot, David Sheeren

Proceedings Volume 8537, 853712 (2012) https://doi.org/10.1117/12.999360

KEYWORDS: Image filtering, Image processing, Image fusion, Sensors, Mathematical morphology, Image analysis, Multispectral imaging, Image quality, Environmental monitoring, Environmental sensing

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Proceedings Article | 23 October 2010 Paper

Supervised super-resolution to improve the resolution of hyperspectral images classification maps

Alberto Villa, Jocelyn Chanussot, Jon Atli Benediktsson, Christian Jutten

Proceedings Volume 7830, 78300J (2010) https://doi.org/10.1117/12.864938

KEYWORDS: Spatial resolution, Hyperspectral imaging, Image classification, Image resolution, Algorithms, Statistical analysis, Image segmentation, Super resolution, Spectral resolution, Remote sensing

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Hyperspectral imaging is a continuously growing area of remote sensing. Hyperspectral data provide a wide spectral range, coupled with a very high spectral resolution, and are suitable for detection and classification of surfaces and chemical elements in the observed image. The main problem with hyperspectral data for these applications is the (relatively) low spatial resolution, which can vary from a few to tens of meters. In the case of classification purposes, the major problem caused by low spatial resolution is related to mixed pixels, i.e., pixels in the image where more than one land cover class is within the same pixel. In such a case, the pixel cannot be considered as belonging to just one class, and the assignment of the pixel to a single class will inevitably lead to a loss of information, no matter what class is chosen. In this paper, a new supervised technique exploiting the advantages of both probabilistic classifiers and spectral unmixing algorithms is proposed, in order to produce land cover maps of improved spatial resolution. The method is in three steps. In a first step, a coarse classification is performed, based on the probabilistic output of a Support Vector Machine (SVM). Every pixel can be assigned to a class, if the probability value obtained in the classification process is greater than a chosen threshold, or unclassified. In the proposed approach it is assumed that the pixels with a low probabilistic output are mixed pixels and thus their classification is addressed in a second step. In the second step, spectral unmixing is performed on the mixed pixels by considering the preliminary results of the coarse classification step and applying a Fully Constrained Least Squares (FCLS) method to every unlabeled pixel, in order to obtain the abundances fractions of each land cover type. Finally, in a third step, spatial regularization by Simulated Annealing is performed to obtain the resolution improvement. Experiments were carried out on a real hyperspectral data set. The results are good both visually and numerically and show that the proposed method clearly outperforms common hard classification methods when the data contain mixed pixels.

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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SPIE Journal Paper | 1 July 2008

Practical use of receiver operating characteristic analysis to assess the performances of defect detection algorithms

Yann Le Meur, Jean-Michel Vignolle, Jocelyn Chanussot

JEI, Vol. 17, Issue 03, 031104, (July 2008) https://doi.org/10.1117/12.10.1117/1.2952590

KEYWORDS: Detection and tracking algorithms, Target detection, Defect detection, Inspection, Receivers, Fuzzy logic, Image processing, Binary data, Imaging systems, Image quality

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Proceedings Article | 29 May 2007 Paper

A practical use of ROC analysis to assess the performances of defects detection algorithms

Yann Le Meur, Jean-Michel Vignolle, Jocelyn Chanussot

Proceedings Volume 6356, 635616 (2007) https://doi.org/10.1117/12.737149

KEYWORDS: Target detection, Detection and tracking algorithms, Defect detection, Inspection, Binary data, Image processing, X-ray detectors, Control systems, Sensors, Fuzzy logic

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Proceedings Article | 19 October 2005 Paper

Classification of remote sensing imagery with high spatial resolution

Mathieu Fauvel, Jon Aevar Palmason, Jon Atli Benediktsson, Jocelyn Chanussot, Johannes Sveinsson

Proceedings Volume 5982, 598201 (2005) https://doi.org/10.1117/12.637224

KEYWORDS: Feature extraction, Image classification, Neural networks, Remote sensing, Independent component analysis, Spatial resolution, Principal component analysis, Mathematical morphology, Fuzzy logic, Image resolution

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Proceedings Article | 27 February 2004 Paper

Use of time-scale representations for the analysis of seismic signals

Antoine Roueff, Jocelyn Chanussot, Jerome Mars

Proceedings Volume 5266, (2004) https://doi.org/10.1117/12.515762

KEYWORDS: Sensors, Polarization, Image segmentation, Continuous wavelet transforms, Phase shifts, Time-frequency analysis, Wave propagation, Image processing algorithms and systems, Spatial frequencies, Wavelets

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Showing 5 of 14 publications

Conference Committee Involvement (17)

Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX

16 September 2024 | Edinburgh, United Kingdom

Image and Signal Processing for Remote Sensing XXIX

4 September 2023 | Amsterdam, Netherlands

Image and Signal Processing for Remote Sensing XXVIII

5 September 2022 | Berlin, Germany

Image and Signal Processing for Remote Sensing XXVII

13 September 2021 | Online Only, Spain

Image and Signal Processing for Remote Sensing XXVI

21 September 2020 | Online Only, United Kingdom

Image and Signal Processing for Remote Sensing XXV

9 September 2019 | Strasbourg, France