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PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery / Xian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)
Titre : PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery Type de document : Article/Communication Auteurs : Xian Sun, Auteur ; Peijin Wang, Auteur ; Cheng Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 50 - 65 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] objet géographique complexe
[Termes IGN] prise en compte du contexte
[Termes IGN] rectangle englobant minimumRésumé : (auteur) In recent years, deep learning-based algorithms have brought great improvements to rigid object detection. In addition to rigid objects, remote sensing images also contain many complex composite objects, such as sewage treatment plants, golf courses, and airports, which have neither a fixed shape nor a fixed size. In this paper, we validate through experiments that the results of existing methods in detecting composite objects are not satisfying enough. Therefore, we propose a unified part-based convolutional neural network (PBNet), which is specifically designed for composite object detection in remote sensing imagery. PBNet treats a composite object as a group of parts and incorporates part information into context information to improve composite object detection. Correct part information can guide the prediction of a composite object, thus solving the problems caused by various shapes and sizes. To generate accurate part information, we design a part localization module to learn the classification and localization of part points using bounding box annotation only. A context refinement module is designed to generate more discriminative features by aggregating local context information and global context information, which enhances the learning of part information and improve the ability of feature representation. We selected three typical categories of composite objects from a public dataset to conduct experiments to verify the detection performance and generalization ability of our method. Meanwhile, we build a more challenging dataset about a typical kind of complex composite objects, i.e., sewage treatment plants. It refers to the relevant information from authorities and experts. This dataset contains sewage treatment plants in seven cities in the Yangtze valley, covering a wide range of regions. Comprehensive experiments on two datasets show that PBNet surpasses the existing detection algorithms and achieves state-of-the-art accuracy. Numéro de notice : A2021-105 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.12.015 Date de publication en ligne : 16/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.12.015 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96891
in ISPRS Journal of photogrammetry and remote sensing > vol 173 (March 2021) . - pp 50 - 65[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021031 SL Revue Centre de documentation Revues en salle Disponible 081-2021033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt
Titre : Do semantic parts emerge in convolutional neural networks? Type de document : Article/Communication Auteurs : Abel Gonzalez-Garcia, Auteur ; Davide Modolo, Auteur ; Vittorio Ferrari, Auteur Année de publication : 2018 Article en page(s) : pp 476 - 494 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] reconnaissance d'objets
[Termes IGN] rectangle englobant minimum
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) Semantic object parts can be useful for several visual recognition tasks. Lately, these tasks have been addressed using Convolutional Neural Networks (CNN), achieving outstanding results. In this work we study whether CNNs learn semantic parts in their internal representation. We investigate the responses of convolutional filters and try to associate their stimuli with semantic parts. We perform two extensive quantitative analyses. First, we use ground-truth part bounding-boxes from the PASCAL-Part dataset to determine how many of those semantic parts emerge in the CNN. We explore this emergence for different layers, network depths, and supervision levels. Second, we collect human judgements in order to study what fraction of all filters systematically fire on any semantic part, even if not annotated in PASCAL-Part. Moreover, we explore several connections between discriminative power and semantics. We find out which are the most discriminative filters for object recognition, and analyze whether they respond to semantic parts or to other image patches. We also investigate the other direction: we determine which semantic parts are the most discriminative and whether they correspond to those parts emerging in the network. This enables to gain an even deeper understanding of the role of semantic parts in the network. Numéro de notice : A2018-408 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-017-1048-0 Date de publication en ligne : 17/10/2017 En ligne : https://doi.org/10.1007/s11263-017-1048-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90882
in International journal of computer vision > vol 126 n° 5 (May 2018) . - pp 476 - 494[article]
Titre : 3D visibility analysis indicating quantitative and qualitative aspects of the visible space Type de document : Article/Communication Auteurs : D. Golub, Auteur ; Y. Doytsher, Auteur ; D. Fisher-Gewirtzman, Auteur Année de publication : 2018 Article en page(s) : pp 134 - 146 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] données localisées 3D
[Termes IGN] Matlab
[Termes IGN] milieu urbain
[Termes IGN] rectangle englobant minimum
[Termes IGN] visibilité entre points
[Termes IGN] voxelRésumé : (auteur) This paper presents the development of a 3D visibility analysis model that consist a combination of objective calculations and a subjective evaluation, representing the value of the view and its possible impact on the perception of a viewer. The model, developed in Matlab, has default weightings for different elements of the view, which can be changed in accordance to future users. A bounding box, defined as working area consisting buildings and topography, is divided into equal-size voxels and sub-voxels for higher accuracy. This model may be further developed for use in practice to support a sustainable future urban environment. Numéro de notice : A2018-180 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/00396265.2016.1253523 Date de publication en ligne : 20/01/2017 En ligne : https://doi.org/10.1080/00396265.2016.1253523 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89824
in Survey review > vol 50 n° 359 (March 2018) . - pp 134 - 146[article]
Titre : Tubelets : Unsupervised action proposals from spatiotemporal super-voxels Type de document : Article/Communication Auteurs : Mihir Jain, Auteur ; Jan van Gemert, Auteur ; Hervé Jégou, Auteur ; Patrick Bouthemy, Auteur ; Cees G. M. Snoek, Auteur Année de publication : 2017 Article en page(s) : pp 287 - 311 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] données spatiotemporelles
[Termes IGN] reconnaissance de gestes
[Termes IGN] rectangle englobant minimum
[Termes IGN] séquence d'images
[Termes IGN] voxelRésumé : (Auteur) This paper considers the problem of localizing actions in videos as sequences of bounding boxes. The objective is to generate action proposals that are likely to include the action of interest, ideally achieving high recall with few proposals. Our contributions are threefold. First, inspired by selective search for object proposals, we introduce an approach to generate action proposals from spatiotemporal super-voxels in an unsupervised manner, we call them Tubelets. Second, along with the static features from individual frames our approach advantageously exploits motion. We introduce independent motion evidence as a feature to characterize how the action deviates from the background and explicitly incorporate such motion information in various stages of the proposal generation. Finally, we introduce spatiotemporal refinement of Tubelets, for more precise localization of actions, and pruning to keep the number of Tubelets limited. We demonstrate the suitability of our approach by extensive experiments for action proposal quality and action localization on three public datasets: UCF Sports, MSR-II and UCF101. For action proposal quality, our unsupervised proposals beat all other existing approaches on the three datasets. For action localization, we show top performance on both the trimmed videos of UCF Sports and UCF101 as well as the untrimmed videos of MSR-II. Numéro de notice : A2017-812 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-017-1023-9 En ligne : https://doi.org/10.1007/s11263-017-1023-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89252
in International journal of computer vision > vol 124 n° 3 (15 September 2017) . - pp 287 - 311[article]
Titre : Novel shape indices for vector landscape pattern analysis Type de document : Article/Communication Auteurs : C. Zhang, Auteur ; Peter M. Atkinson, Auteur Année de publication : 2016 Article en page(s) : pp 2442 - 2461 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de données
[Termes IGN] anisotropie
[Termes IGN] données vectorielles
[Termes IGN] indice de détection
[Termes IGN] interprétation automatique
[Termes IGN] rectangle englobant minimum
[Termes IGN] représentation des données
[Termes IGN] traitement automatique de donnéesRésumé : (Auteur) The formation of an anisotropic landscape is influenced by natural and/or human processes, which can then be inferred on the basis of geometric indices. In this study, two minimal bounding rectangles in consideration of the principles of mechanics (i.e. minimal width bounding (MWB) box and moment bounding (MB) box) were introduced. Based on these boxes, four novel shape indices, namely MBLW (the length-to-width ratio of MB box), PAMBA (area ratio between patch and MB box), PPMBP (perimeter ratio between patch and MB box) and ODI (orientation difference index between MB and MWB boxes), were introduced to capture multiple aspects of landscape features including patch elongation, patch compactness, patch roughness and patch symmetry. Landscape pattern was, thus, quantified by considering both patch directionality and patch shape simultaneously, which is especially suitable for anisotropic landscape analysis. The effectiveness of the new indices were tested with real landscape data consisting of three kinds of saline soil patches (i.e. the elongated shaped slightly saline soil class, the circular or half-moon shaped moderately saline soil, and the large and complex severely saline soil patches). The resulting classification was found to be more accurate and robust than that based on traditional shape complexity indices. Numéro de notice : A2016-757 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1179313 En ligne : http://dx.doi.org/10.1080/13658816.2016.1179313 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82422
in International journal of geographical information science IJGIS > vol 30 n° 11-12 (November - December 2016) . - pp 2442 - 2461[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2016061 RAB Revue Centre de documentation En réserve L003 Disponible PermalinkFusion of hyperspectral images and digital surface models for urban object extraction / Janja Avbelj (2016)
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