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Extracting urban landmarks from geographical datasets using a random forests classifier / Yue Lin in International journal of geographical information science IJGIS, vol 33 n°12 (December 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n°12 (December 2019) . - pp 2406 - 2423 Titre : Extracting urban landmarks from geographical datasets using a random forests classifier Type de document : Article/Communication Auteurs : Yue Lin, Auteur ; Yuyang Cai, Auteur ; Yue Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 2406 - 2423 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] point de repère [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] représentation mentale spatiale [Termes descripteurs IGN] saillance [Termes descripteurs IGN] Shenzhen [Termes descripteurs IGN] ville Résumé : (auteur) Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifier combining with the synthetic minority oversampling technique (SMOTE) in urban landmark extraction. Both GIS and social sensing data are employed to quantify the structural and cognitive salience of the examined urban features, which are available from basic spatial databases or mainstream web service application programming interfaces (APIs). The results show that the SMOTE-RF model performs well in urban landmark extraction, with the values of recall, precision, F-measure and AUC reaching 0.851, 0.831, 0.841 and 0.841, respectively. Additionally, this method is suitable for both large and small geographical datasets. The ranking of variable importance given by this model further indicates that certain cognitive measures – such as feature class, Weibo popularity and Bing popularity – can serve as crucial factors for determining a landmark. The optimal variable combination for landmark extraction is also acquired, which might provide support for eliminating the variable selection requirement in other landmark extraction methods. Numéro de notice : A2019-426 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1620238 date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1080/13658816.2019.1620238 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93559 [article]

Saliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 57 n° 10 (October 2019) . - pp 7365 - 7377 Titre : Saliency-guided deep neural networks for SAR image change detection Type de document : Article/Communication Auteurs : Jie Geng, Auteur ; Xiaorui Ma, Auteur ; Xiaojun Zhou, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 7365 - 7377 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] filtre de déchatoiement [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] logique floue [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] saillance [Termes descripteurs IGN] télédétection en hyperfréquence Mots-clés libres : hierarchical fuzzy C-means clustering (HFCM) Résumé : (auteur) Change detection is an important task to identify land-cover changes between the acquisitions at different times. For synthetic aperture radar (SAR) images, inherent speckle noise of the images can lead to false changed points, which affects the change detection performance. Besides, the supervised classifier in change detection framework requires numerous training samples, which are generally obtained by manual labeling. In this paper, a novel unsupervised method named saliency-guided deep neural networks (SGDNNs) is proposed for SAR image change detection. In the proposed method, to weaken the influence of speckle noise, a salient region that probably belongs to the changed object is extracted from the difference image. To obtain pseudotraining samples automatically, hierarchical fuzzy C-means (HFCM) clustering is developed to select samples with higher probabilities to be changed and unchanged. Moreover, to enhance the discrimination of sample features, DNNs based on the nonnegative- and Fisher-constrained autoencoder are applied for final detection. Experimental results on five real SAR data sets demonstrate the effectiveness of the proposed approach. Numéro de notice : A2019-536 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2913095 date de publication en ligne : 19/05/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2913095 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94154 [article]

Image retrieval based on saliency for urban image contents / Kamel Guissous (2017)  

Public Titre : Image retrieval based on saliency for urban image contents Type de document : Article/Communication Auteurs : Kamel Guissous , Auteur ; Valérie Gouet-Brunet , Auteur Congrès : IPTA 2017, 7th International Conference on Image Processing Theory, Tools and Applications (28 novembre - 1er décembre 2017), Commanditaire Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Importance : 6 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] saillance [Termes descripteurs IGN] scène urbaine Résumé : (auteur) With the increase of image datasets size and of descriptors complexity in Content-Based Image Retrieval (CBIR) and Computer Vision, it is essential to find a way to limit the amount of manipulated data, while keeping its quality. Instead of treating the entire image, the selection of regions which hold the essence of information is a relevant option to reach this goal. As the visual saliency aims at highlighting the areas of the image which are the most important for a given task, in this paper we propose to exploit visual saliency maps to prune the most salient image features. A novel visual saliency approach based on the local distribution analysis of the edges orientation, particularly dedicated to structured contents, such as street view images of urban environments, is proposed. It is evaluated for CBIR according to three criteria: quality of retrieval, volume of manipulated features and computation time. The proposal can be exploited into various applications involving large sets of local visual features; here it is experimented within two applications: cross-domain image retrieval and image-based vehicle localisation. Numéro de notice : C2017-040 Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IPTA.2017.8310131 date de publication en ligne : 12/03/2018 En ligne : https://doi.org/10.1109/IPTA.2017.8310131 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91922

Road extraction based on snakes and sophisticated line extraction / Ivan Laptev (1997)

Public Titre : Road extraction based on snakes and sophisticated line extraction Type de document : Mémoire Auteurs : Ivan Laptev, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 1997 Importance : 67 p. Format : 21 x 30 cm Note générale : bibliography Master of Science, Royal Institute of Technology, Stockholm Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme snake [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] saillance Résumé : (auteur) The extraction of roads from aerial and satellite images is an important task within cartography and planning of new road networks. The automation of this task is highly motivated by the expected increase of the speed and the precision of extraction. This work considers automatic road extraction from single aerial images of high resolution. It is based on two previously developed approaches: The first one is the differential geometric approach for extraction of linear features; The second - contour extraction based on Active Contour Models, also called snakes. Whereas the first approach is fully-automatic, the second was previously mostly used for semi-automatic tasks which require the control of a human operator. This work combines both techniques and adapt them in the method for fully-automatic road extraction. According to the used strategy, the hypotheses for most salient roads in images of rural scenes are generated and verified first. Then, based on the ends of extracted roads the hypotheses for other roads are stated. The developed snake-based technique for the verification of these hypotheses enables recognition of partially occluded and shadowed roads as well as some roads passing through road crossings. The presented results of the developed approach show that the reliable extraction of roads which images are disturbed by surrounding objects is in many cases possible without the explicit knowledge about these objects. This is a big advantage since the automatic recognition of buildings, vegetation etc., is a very complicated problem by itself. Note de contenu : Introduction 1 - Survey of Related Work 2 - Objectives and Limitations 3 - Theory of Snakes 4 - Road Extraction Loop 5 - Matching of the Road Model 6 - Results Conclusion Numéro de notice : 21689 Thématique : IMAGERIE Nature : Mémoire DEA divers Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90926