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Your search keyword '"Chanussot, Jocelyn"' showing total 33 results
Start Over Author "Chanussot, Jocelyn" Publication Type Books
33 results on '"Chanussot, Jocelyn"'

1. Contributors

Author

Ambrozio Dias, Philipe, primary, Aptoula, Erchan, additional, Arndt, Jacob, additional, Banerjee, Biplab, additional, Chanussot, Jocelyn, additional, Chen, Yuzhou, additional, Choi, Jae Won, additional, Daba, Natnael, additional, Ertürk, Alp, additional, Gel, Yulia R., additional, Hänsch, Ronny, additional, Hong, Danfeng, additional, Jeppu, Yogananda, additional, LaHaye, Nick, additional, Lee, Hugo, additional, Lexie Yang, H., additional, Li, Jun, additional, Lobry, Sylvain, additional, Lunga, Dalton, additional, Mahalanobis, Abhijit, additional, Mahdianpari, Masoud, additional, McIntosh, Bruce, additional, Mohammadimanesh, Fariba, additional, Niyogi, Dev, additional, Pal, Debabrata, additional, Potnis, Abhishek, additional, Prasad, Saurabh, additional, Singh, Manmeet, additional, Su, Yuanchao, additional, Sui, Chenhong, additional, Taskin, Gulsen, additional, Tuia, Devis, additional, Wohlgemuth, Jason, additional, Xu, Xiang, additional, Yokoya, Naoto, additional, Zhang, Shaoquan, additional, and Zhou, Xiong, additional

Published
2024
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2. Introduction

Author

Prasad, Saurabh, primary, Chanussot, Jocelyn, additional, and Li, Jun, additional

Published
2024
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5. List of contributors

Author

Batselier, Kim, primary, Bi, Yingyue, additional, Boulanger, Jérémie, additional, Boyer, Rémy, additional, Caiafa, Cesar F., additional, Chanussot, Jocelyn, additional, Chatzichristos, Christos, additional, Chen, Cong, additional, Cohen, Nadav, additional, Cui, Xudong, additional, de Almeida, André L.F., additional, Erol, Aybüke, additional, Fang, Yiming, additional, Favier, Gérard, additional, Hunyadi, Borbála, additional, Jawanpuria, Pratik, additional, Jiang, Tai-Xiang, additional, Karakasis, Paris A., additional, Karmouda, Ouafae, additional, Kasai, Hiroyuki, additional, Kofidis, Eleftherios, additional, Kolomvakis, Christos, additional, Levine, Yoav, additional, Li, Zechu, additional, Liavas, Athanasios P., additional, Lin, Zhouchen, additional, Liu, Xiao-Yang, additional, Liu, Yipeng, additional, Long, Zhen, additional, Lourakis, George, additional, Lu, Canyi, additional, Lu, Liangfu, additional, Lu, Yingcong, additional, Lykoudis, George, additional, Mishra, Bamdev, additional, Ng, Michael K., additional, Papagiannakos, Ioannis Marios, additional, Ren, Bo, additional, Sharir, Or, additional, Shashua, Amnon, additional, Siaminou, Ioanna, additional, Tsalidis, Christos, additional, Van Eyndhoven, Simon, additional, Van Huffel, Sabine, additional, Walid, Anwar, additional, Wen, Fei, additional, Wies, Noam, additional, Wong, Ngai, additional, Wu, Zebin, additional, Xu, Yang, additional, Yang, Liuqing, additional, Ye, Fei, additional, Yokota, Tatsuya, additional, Zhang, Zhonghao, additional, Zhao, Qibin, additional, Zhao, Xi-Le, additional, Zhou, Pan, additional, Zhu, Ce, additional, and Zniyed, Yassine, additional

Published
2022
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9. Applications in remote sensing—natural landscapes

Author

Bajjouk, Touria, primary, de Boissieu, Florian, additional, Chanussot, Jocelyn, additional, Dout, Sylvain, additional, Dumont, Marie, additional, Féret, Jean-Baptiste, additional, Masson, Théo, additional, Minghelli, Audrey, additional, Mouquet, Pascal, additional, Schmidt, Frédéric, additional, and Mura, Mauro Dalla, additional

Published
2020
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10. Fusion of hyperspectral imaging and LiDAR for forest monitoring

Author

Tusa, Eduardo, primary, Laybros, Anthony, additional, Monnet, Jean-Matthieu, additional, Dalla Mura, Mauro, additional, Barré, Jean-Baptiste, additional, Vincent, Grégoire, additional, Dalponte, Michele, additional, Féret, Jean-Baptiste, additional, and Chanussot, Jocelyn, additional

Published
2020
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16. Scientific Committee

Author

Kallel, Abdelaziz, primary, Gastellu, Jean-Philippe, additional, Dedieu, Gérard, additional, Cherel, Jean-Philippe, additional, Mercier, Grégoire, additional, Ducrot, Danielle, additional, Dupuy, Stéphane, additional, Morel, Jean-Michel, additional, Berthier, Etienne, additional, Jordan, Michel, additional, Fauvel, Mathieu, additional, Chanussot, Jocelyn, additional, Costeraste, Josiane, additional, Raut, Jean-Christophe, additional, Ancellet, Gérard, additional, Roux, Michel, additional, Pastol, Yves, additional, Feret, Jean-Baptiste, additional, Baghdadi, Nicolas, additional, and Zribi, Mehrez, additional

Published
2016
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24. Introduction

Author

Prasad, Saurabh, primary, Bruce, Lori M., additional, and Chanussot, Jocelyn, additional

Published
2011
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31. Rotation-Based Ensemble Classifiers for High-Dimensional Data.

Author

Xia, Junshi, Chanussot, Jocelyn, Du, Peijun, and He, Xiyan

Abstract

In past 20 years, Multiple Classifier System (MCS) has shown great potential to improve the accuracy and reliability of pattern classification. In this chapter, we discuss the major issues of MCS, including MCS topology, classifier generation, and classifier combination, providing a summary of MCS applied to remote sensing image classification, especially in high-dimensional data. Furthermore, the recently rotation-based ensemble classifiers, which encourage both individual accuracy and diversity within the ensemble simultaneously, are presented to classify high-dimensional data, taking hyperspectral and multidate remote sensing images as examples. Rotation-based ensemble classifiers project the original data into a new feature space using feature extraction and subset selection methods to generate the diverse individual classifiers. Two classifiers: Decision Tree (DT) and Support Vector Machine (SVM), are selected as the base classifier. Unsupervised and supervised feature extraction methods are employed in the rotation-based ensemble classifiers. Experimental results demonstrated that rotation-based ensemble classifiers are superior to Bagging, AdaBoost and random-based ensemble classifiers. [ABSTRACT FROM AUTHOR]

Published
2014
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32. Ensemble Strategies for Classifying Hyperspectral Remote Sensing Data.

Author

Ceamanos, Xavier, Waske, Björn, Benediktsson, Jón Atli, Chanussot, Jocelyn, and Sveinsson, Johannes R.

Abstract

The classification of hyperspectral imagery, using multiple classifier systems is discussed and an SVM-based ensemble is introduced. The data set is separated into separate feature subsets using the correlation between the different spectral bands as a criterion. Afterwards, each source is classified separately by an SVM classifier. Finally, the different outputs are used as inputs for final decision fusion that is based on an additional SVM classifier. The results using the proposed strategy are compared to classification results achieved by a single SVM and other well known classifier ensembles, such as random forests, boosting and bagging. [ABSTRACT FROM AUTHOR]

Published
2009
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33. Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments.

Author

Hutchison, David, Kanade, Takeo, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Haindl, Michal, Kittler, Josef, Roli, Fabio, Benediktsson, Jon Atli, Chanussot, Jocelyn, and Fauvel, Mathieu

Abstract

In this paper, we present some recent developments of Multiple Classifiers Systems (MCS) for remote sensing applications. Some standard MCS methods (boosting, bagging, consensus theory and random forests) are briefly described and applied to multisource data (satellite multispectral images, elevation, slope and aspect data) for landcover classification. In the second part, special attention is given to Support Vector Machines (SVM) based algorithms. In particular, the fusion of two classifiers using both spectral and the spatial information is discussed in the frame of hyperspectral remote sensing for the classification of urban areas. In all the cases, MCS provide a significant improvement of the classification accuracies. In order to address new challenges for the analysis of remote sensing data, MCS provide invaluable tools to handle situations with an ever growing complexity. Examples include extraction of multiple features from one data set, use of multi-sensor data, and complementary use of several algorithms in a decision fusion scheme. [ABSTRACT FROM AUTHOR]

Published
2007
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