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Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification / Zitong Wu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1200 - 1213 Titre : Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification Type de document : Article/Communication Auteurs : Zitong Wu, Auteur ; Biao Hou, Auteur ; Licheng Jiao, Auteur Année de publication : 2021 Article en page(s) : pp 1200 - 1213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification contextuelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image radar moirée Résumé : (auteur) Synthetic aperture radar (SAR) image classification is a fundamental research direction in image interpretation. With the development of various intelligent technologies, deep learning techniques are gradually being applied to SAR image classification. In this study, a new SAR classification algorithm known as the multiscale convolutional neural network with an autoencoder regularization joint contextual attention network (MCAR-CAN) is proposed. The MCAR-CAN has two branches: the autoencoder regularization branch and the context attention branch. First, autoencoder regularization is used for the reconstruction of the input to regularize the classification in the autoencoder regularization branch. Multiscale input and an asymmetric structure of the autoencoder branch cause the network more to be focused on classification than on reconstruction. Second, the attention mechanism is used to produce an attention map in which each attention weight corresponds to a context correlation in attention branch. The robust features are obtained by the attention mechanism. Finally, the features obtained by the two branches are spliced for classification. In addition, a new training strategy and a postprocessing method are designed to further improve the classification accuracy. Experiments performed on the data from three SAR images demonstrated the effectiveness and robustness of the proposed algorithm. Numéro de notice : A2021-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3004911 Date de publication en ligne : 07/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3004911 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96918 [article]

Contextual classification using photometry and elevation data for damage detection after an earthquake event / Ewelina Rupnik in European journal of remote sensing, vol 51 n° 1 (2018)  

Public [article]  inEuropean journal of remote sensing > vol 51 n° 1 (2018) . - pp 543 - 557 Titre : Contextual classification using photometry and elevation data for damage detection after an earthquake event Type de document : Article/Communication Auteurs : Ewelina Rupnik , Auteur ; Francesco Nex, Auteur ; Isabella Toschi, Auteur ; Fabio Remondino, Auteur Année de publication : 2018 Projets : 3-projet - voir note / Article en page(s) : pp 543 - 557 Note générale : bibliographie This work was supported by RAPIDMAP, a CONCERT-Japan project, i.e. a European Union (EU) funded project in the International Cooperation Activities under the Capacities Programme the 7th Framework Programme for Research and Technology Development. https://cordis.europa.eu/project/id/266604/reporting Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] cartographie d'urgence [Termes IGN] chaîne de traitement [Termes IGN] champ aléatoire conditionnel [Termes IGN] classification contextuelle [Termes IGN] classification dirigée [Termes IGN] classification non dirigée [Termes IGN] détection de changement [Termes IGN] dommage matériel [Termes IGN] image aérienne [Termes IGN] modèle numérique de surface [Termes IGN] orthoimage [Termes IGN] photométrie [Termes IGN] prise en compte du contexte [Termes IGN] zone urbaine Résumé : (auteur) This research presents a processing workflow to automatically find damaged building areas in an urban context. The input data requirements are high-resolution multi-view images, acquired from airborne platform. The elevations are derived from a dense surface model generated with photogrammetric methods. With the principal objective of rapid response in emergency situations, two different processing roadmaps are proposed, semi-supervised and unsupervised. Both of them follow a two-step workflow of building detection and building health estimation. Optionally, cadastral layers may serve as a-priori knowledge on building location. The semi-supervised approach involves a data training step, while the unsupervised approach exploits the similarities and dissimilarities between sets of features calculated over the detected buildings. The change detection task is formulated as a classification task defined over a conditional random field. The algorithms are evaluated using two datasets (Vexcel and Midas cameras) and results are compared with ground truth data and specific metrics. Numéro de notice : A2018-664 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2018.1458584 Date de publication en ligne : 16/05/2018 En ligne : https://doi.org/10.1080/22797254.2018.1458584 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94250 [article]

An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings / Sergio J. Rey in Transactions in GIS, vol 21 n° 4 (August 2017)  

Public [article]  inTransactions in GIS > vol 21 n° 4 (August 2017) . - pp 796 - 810 Titre : An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings Type de document : Article/Communication Auteurs : Sergio J. Rey, Auteur ; Philip Stephens, Auteur ; Jason Laura, Auteur Année de publication : 2017 Article en page(s) : pp 796 - 810 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] autocorrélation spatiale [Termes IGN] carte choroplèthe [Termes IGN] classification contextuelle [Termes IGN] données massives [Termes IGN] échantillon [Termes IGN] méthode de Monte-Carlo [Termes IGN] précision de la classification [Termes IGN] simulation [Termes IGN] traitement de données localisées Résumé : (Auteur) Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of map classifiers methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets. Numéro de notice : A2017-630 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12236 En ligne : http://dx.doi.org/10.1111/tgis.12236 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86945 [article]

Superpixel-based graphical model for remote sensing image mapping / Guangyun Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 11 (November 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 11 (November 2015) . - pp 5861 - 5871 Titre : Superpixel-based graphical model for remote sensing image mapping Type de document : Article/Communication Auteurs : Guangyun Zhang, Auteur ; Xiuping Jia, Auteur ; Jiankun Hu, Auteur Année de publication : 2015 Article en page(s) : pp 5861 - 5871 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification contextuelle [Termes IGN] classification pixellaire [Termes IGN] décomposition du pixel [Termes IGN] image multibande [Termes IGN] modèle sémantique de données [Termes IGN] segmentation d'image Résumé : (Auteur) Object-oriented remote sensing image classification is becoming more and more popular because it can integrate spatial information from neighboring regions of different shapes and sizes into the classification procedure to improve the mapping accuracy. However, object identification itself is difficult and challenging. Superpixels, which are groups of spatially connected similar pixels, have the scale between the pixel level and the object level and can be generated from oversegmentation. In this paper, we establish a new classification framework using a superpixel-based graphical model. Superpixels instead of pixels are applied as the basic unit to the graphical model to capture the contextual information and the spatial dependence between the superpixels. The advantage of this treatment is that it makes the classification less sensitive to noise and segmentation scale. The contribution of this paper is the application of a graphical model to remote sensing image semantic segmentation. It is threefold. 1) Gradient fusion is applied to multispectral images before the watershed segmentation algorithm is used for superpixel generation. 2) A probabilistic fusion method is designed to derive node potential in the superpixel-based graphical model to address the problem of insufficient training samples at the superpixel level. 3) A boundary penalty between the superpixels is introduced in the edge potential evaluation. Experiments on three real data sets were conducted. The results show that the proposed method performs better than the related state-of-the-art methods tested. Numéro de notice : A2015-770 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2423688 Date de publication en ligne : 08/06/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2423688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78826 [article]

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Contextual classification of point cloud data by exploiting individual 3d neigbourhoods / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3 W4 (March 2015)    

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > II-3 W4 (March 2015) . - pp 271 - 278 Titre : Contextual classification of point cloud data by exploiting individual 3d neigbourhoods Type de document : Article/Communication Auteurs : Martin Weinmann, Auteur ; A. Schmidt, Auteur ; Clément Mallet , Auteur ; Stefan Hinz, Auteur ; Franz Rottensteiner, Auteur ; Boris Jutzi, Auteur Année de publication : 2015 Conférence : ISPRS 2015, PIA 2015 - HRIGI 2015 Joint ISPRS conference 25/03/2015 27/03/2015 Munich Allemagne ISPRS OA Annals Article en page(s) : pp 271 - 278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse d'image orientée objet [Termes IGN] classification contextuelle [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] scène urbaine [Termes IGN] semis de points [Termes IGN] voisinage (relation topologique) [Termes IGN] zone urbaine Résumé : (auteur) The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification. Numéro de notice : A2015--052 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-II-3-W4-271-2015 Date de publication en ligne : 11/03/2015 En ligne : http://dx.doi.org/10.5194/isprsannals-II-3-W4-271-2015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82698 [article]

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Supervised image classification by contextual adaboost based on posteriors in neighborhoods / Ryuei Nishii in IEEE Transactions on geoscience and remote sensing, vol 43 n° 11 (November 2005)  

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Intégration du contexte spatio-temporel dans le contrôle d'accès basé sur les rôles / R. Thion in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 10 n° 4 (juillet -août 2005)

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Combining spectral and spatial information into hidden Markov models for unsupervised image classification / B. Tso in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)  

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Le boosting : essai d'une méthode de classification adaptée à la télédétection / David Levrel in Revue internationale de géomatique, vol 14 n° 3 - 4 (septembre 2004 – février 2005)  

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A cognitive pyramid for contextual classification of remote sensing images / E. Binaghi in IEEE Transactions on geoscience and remote sensing, vol 41 n° 12 (December 2003)  

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