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Urban construction waste with VHR remote sensing using multi-feature analysis and a hierarchical segmentation method / Qiang Chen in Remote sensing, vol 13 n° 1 (January 2021)  

Public [article]  inRemote sensing > vol 13 n° 1 (January 2021) . - n° 158 Titre : Urban construction waste with VHR remote sensing using multi-feature analysis and a hierarchical segmentation method Type de document : Article/Communication Auteurs : Qiang Chen, Auteur ; Qianhao Cheng, Auteur ; Jinfei Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 158 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] construction [Termes descripteurs IGN] déchet [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] gestion urbaine [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] morphologie [Termes descripteurs IGN] Pékin (Chine) [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] urbanisation Résumé : (auteur) With rapid urbanization, the disposal and management of urban construction waste have become the main concerns of urban management. The distribution of urban construction waste is characterized by its wide range, irregularity, and ease of confusion with the surrounding ground objects, such as bare soil, buildings, and vegetation. Therefore, it is difficult to extract and identify information related to urban construction waste by using the traditional single spectral feature analysis method due to the problem of spectral confusion between construction waste and the surrounding ground objects, especially in the context of very-high-resolution (VHR) remote sensing images. Considering the multi-feature analysis method for VHR remote sensing images, we propose an optimal method that combines morphological indexing and hierarchical segmentation to extract the information on urban construction waste in VHR images. By comparing the differences between construction waste and the surrounding ground objects in terms of the spectrum, geometry, texture, and other features, we selected an optimal feature subset to improve the separability of the construction waste and other objects; then, we established a classification model of knowledge rules to achieve the rapid and accurate extraction of construction waste information. We also chose two experimental areas of Beijing to validate our algorithm. By using construction waste separability quality evaluation indexes, the identification accuracy of construction waste in the two study areas was determined to be 96.6% and 96.2%, the separability indexes of the construction waste and buildings reached 1.000, and the separability indexes of the construction waste and vegetation reached 1.000 and 0.818. The experimental results show that our method can accurately identify the exposed construction waste and construction waste covered with a dust screen, and it can effectively solve the problem of spectral confusion between the construction waste and the bare soil, buildings, and vegetation. Numéro de notice : A2021-073 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010158 date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.3390/rs13010158 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96809 [article]

Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 151 - 165 Titre : Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density Type de document : Article/Communication Auteurs : Yuan Li, Auteur ; Bo Wu, Auteur ; Xuming Ge, Auteur Année de publication : 2019 Article en page(s) : pp 151 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] champ aléatoire conditionnel [Termes descripteurs IGN] classification [Termes descripteurs IGN] classification basée sur les régions [Termes descripteurs IGN] densité des points [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Hong-Kong [Termes descripteurs IGN] modèle 3D de l'espace urbain [Termes descripteurs IGN] Paris (75) [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] segmentation par croissance de régions [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] semis de points Résumé : (Auteur) Objects are formed by various structures and such structural information is essential for the identification of objects, especially for street facilities presented by mobile laser scanning (MLS) data with abundant details. However, due to the large volume of data, large variations in point density, noise and complexity of scanned scenes, the achievement of effective decomposition of objects into physical meaningful structures remains a challenge issue. And structural information has been rarely considered to improve the accuracy of distinguishing between objects with global or local similarity, such as traffic signs and traffic lights. Therefore, we propose a structural segmentation and classification method for MLS point clouds that is efficient and robust to variations in point density and complex urban scenes. During the segmentation stage, a novel region growing approach and a multi-size supervoxel segmentation algorithm robust to noise and varying density are combined to extract effective local shape descriptors. Structural components with physically meaningful labels are generated via structural labelling and clustering. During the classification stage, we consider the structural information at various scales and locations and encode it into a conditional random-field model for unary and pairwise inferences. High-order potentials are also introduced into the conditional random field to eliminate regional label noise. These high-order potentials are defined upon regions independent of connection relationships and can therefore take effect on isolated nodes. Experiments with two MLS datasets of typical urban scenes in Paris and Hong Kong were used to evaluate the performance of the proposed method. Nine and eleven different object classes were recognized from these two datasets with overall accuracies of 97.13% and 95.79%, respectively, indicating the effectiveness of the proposed method of interpreting complex urban scenes from point clouds with large variations in point density. Compared with previous studies on the Paris dataset, our method was able to recognize more classes and obtained a mean F1-score of 72.70% of seven common classes, being higher than the best of previous results. Numéro de notice : A2019-262 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.007 date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93075 [article]

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Automatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 12 (December 2018) . - pp 7369 - 7387 Titre : Automatic building rooftop extraction from aerial images via hierarchical RGB-D priors Type de document : Article/Communication Auteurs : Shibiao Xu, Auteur ; Xingjia Pan, Auteur ; Er Li, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 7369 - 7387 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] champ aléatoire conditionnel [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] itération [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] toit [Termes descripteurs IGN] zone saillante 3D Résumé : (auteur) Accurate building rooftop extraction from high-resolution aerial images is of crucial importance in a wide range of applications. Owing to the varying appearance and large-scale range of scene objects, especially for building rooftops in different scales and heights, single-scale or individual prior-based extraction technique is insufficient in pursuing efficient, generic, and accurate extraction results. The trend toward integrating multiscale or several cue techniques appears to be the best way; thus, such integration is the focus of this paper. We first propose a novel salient rooftop detector integrating four correlative RGB-D priors (depth cue, uniqueness prior, shape prior, and transition surface prior) for improved rooftop extraction to address the preceding complex issues mentioned. Then, these correlative cues are computed from image layers created by our multilevel segmentation and further fused into the state-of-the-art high-order conditional random field (CRF) framework to locate the rooftop. Finally, an iterative optimization strategy is applied for high-quality solving, which can robustly handle varying appearance of building rooftops. Performance evaluations in the SZTAKI-INRIA benchmark data sets show that our method outperforms the traditional color-based algorithm and the original high-order CRF algorithm and its variants. The proposed algorithm is also evaluated and found to produce consistently satisfactory results for various large-scale, real-world data sets. Numéro de notice : A2018-558 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2850972 date de publication en ligne : 26/07/2018 En ligne : http://dx.doi.org/10.1109/TGRS.2018.2850972 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91664 [article]

Toward evaluating multiscale segmentations of high spatial resolution remote sensing images / Xueliang Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3694 - 3706 Titre : Toward evaluating multiscale segmentations of high spatial resolution remote sensing images Type de document : Article/Communication Auteurs : Xueliang Zhang, Auteur ; Pengfeng Xiao, Auteur ; Xuezhi Feng, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 3694 - 3706 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image numérique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] analyse multicritère [Termes descripteurs IGN] Chine [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] segmentation multirésolution Résumé : (Auteur) Object-based analysis of high spatial resolution remote sensing images addresses the matter of multiscale segmentation. However, existing segmentation evaluation methods mainly focus on single-scale segmentation. In this paper, we examine the issue of supervised multiscale segmentation evaluation and propose two discrepancy measures to determine the manner in which geographic objects are delineated by multiscale segmentations. A QuickBird scene in Hangzhou, China, is used to conduct the evaluation. The results reveal the effectiveness of the proposed measures, in terms of method comparison and parameter optimization, for multiscale segmentation of high spatial resolution images. Moreover, meaningful indications for selecting suitable multiple segmentation scales are presented. The proposed measures are applicable to performance evaluation and parameter optimization for multiscale segmentation algorithms. Numéro de notice : A2015-320 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76573 [article]

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Object-based hyperspectral classification of urban areas using marker-based hierarchical segmentation / Davood Akbari in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 10 (October 2014) . - pp 963 - 970 Titre : Object-based hyperspectral classification of urban areas using marker-based hierarchical segmentation Type de document : Article/Communication Auteurs : Davood Akbari, Auteur ; Abdolreza Safari, Auteur ; Saeid Homayouni, Auteur Année de publication : 2014 Article en page(s) : pp 963 - 970 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] classification spectrale [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] segmentation hiérarchique [Termes descripteurs IGN] zone urbaine Résumé : (auteur)An effective approach to spectral-spatial classification has been achieved using Hierarchical SEGmentation (HSEG) by Tarabalka et al. (2009 and 2010). Our goal is to improve this approach to the classification of hyperspectral images in urban areas. The first step of our proposed method is to segment the spectral images using a novel marker-based HSEG, method. The spatial features from segmented images are then extracted. Spatial information such as the area, entropy, shape, adjacency, and relation features constitute the components of feature space. Last, using both spectral and spatial features, the image objects are classified by a support vector machine (SVM) classifier. Three image data-sets were used to test this method. The results of our experiment indicate that the main advantage of the proposed method, compared to the previous HSEG-based approach, is that it increases classification accuracy by selecting the appropriate contextual features of different objects. Numéro de notice : A2014-673 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.10.963 En ligne : https://doi.org/10.14358/PERS.80.10.963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75153 [article]

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Approches multi-hiérarchiques pour l'analyse d'images de télédétection / Camille Kurtz in Revue Française de Photogrammétrie et de Télédétection, n° 205 (Janvier 2014)

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Fast hierarchical segmentation of high-resolution remote sensing images with adaptative edge penalty / Xuellang Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 1 (January 2014)  

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Hierarchical segmentation-based software for cover classification analyses of seabed images (Seascape) / Nuria Teixido in Marine Ecology Progress Series (MEPS), n° 431 ([09/06/2011])    

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Stéréovision par zone outils et structure d’un système expert / Yannick Remion (1988)

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