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Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images / Chen Zheng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10555 - 10574 Titre : Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images Type de document : Article/Communication Auteurs : Chen Zheng, Auteur ; Yun Zhang, Auteur ; Leiguang Wang, Auteur Année de publication : 2021 Article en page(s) : pp 10555 - 10574 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] champ aléatoire de Markov [Termes IGN] granularité d'image [Termes IGN] segmentation sémantique [Termes IGN] texture d'image Résumé : (auteur) Semantic segmentation is one of the most important tasks in remote sensing. However, as spatial resolution increases, distinguishing the homogeneity of each land class and the heterogeneity between different land classes are challenging. The Markov random field model (MRF) is a widely used method for semantic segmentation due to its effective spatial context description. To improve segmentation accuracy, some MRF-based methods extract more image information by constructing the probability graph with pixel or object granularity units, and some other methods interpret the image from different semantic perspectives by building multilayer semantic classes. However, these MRF-based methods fail to capture the relationship between different granularity features extracted from the image and hierarchical semantic classes that need to be interpreted. In this article, a new MRF-based method is proposed to incorporate the multigranularity information and the multilayer semantic classes together for semantic segmentation of remote sensing images. The proposed method develops a framework that builds a hybrid probability graph on both pixel and object granularities and defines a multiclass-layer label field with hierarchical semantic over the hybrid probability graph. A generative alternating granularity inference is suggested to provide the result by iteratively passing and updating information between different granularities and hierarchical semantics. The proposed method is tested on texture images, different remote sensing images obtained by the SPOT5, Gaofen-2, GeoEye, and aerial sensors, and Pavia University hyperspectral image. Experiments demonstrate that the proposed method shows a better segmentation performance than other state-of-the-art methods. Numéro de notice : A2021-873 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3033293 Date de publication en ligne : 11/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3033293 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99132 [article]

Sea-land segmentation using deep learning techniques for Landsat-8 OLI imagery / Ting Yang in Marine geodesy, Vol 43 n° 2 (March 2020)  

Public [article]  inMarine geodesy > Vol 43 n° 2 (March 2020) . - pp 105 - 133 Titre : Sea-land segmentation using deep learning techniques for Landsat-8 OLI imagery Type de document : Article/Communication Auteurs : Ting Yang, Auteur ; Zhonghua Hong, Auteur ; Yun Zhang, Auteur Année de publication : 2020 Article en page(s) : pp 105 - 133 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image Landsat-OLI [Termes IGN] littoral [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique [Termes IGN] trait de côte Résumé : (auteur) Automated coastline extraction from optical satellites is fundamental to coastal mapping, and sea-land segmentation is the core technology of coastline extraction. Deep convolutional neural networks (DCNNs) have performed well in semantic segmentation in recent years. However, sea-land segmentation using deep learning techniques remains a challenging task, due to the lack of a benchmark dataset and the difficulty of deciding which semantic segmentation model to use. We present a comparative framework of sea-land segmentation to Landsat-8 OLI imagery via semantic segmentation in deep learning techniques. Three issues are investigated: (1) constructing a sea-land benchmark dataset using Landsat-8 Operational Land Imager (OLI) imagery consisting of 18,000 km2 of coastline around China; (2) evaluating the feasibility and performance of sea-land segmentation by comparing the accuracy assessment, time complexity, spatial complexity and stability of state-of-the-art DCNNs methods; (3) choosing the most suitable semantic segmentation model for sea-land segmentation in accordance with Akaike information criterion (AIC) and Bayesian information criterion (BIC) model selection. Results show that the average test accuracy achieves over 99% accuracy, and the mean Intersection over Unions (mean IoU) is above 92%. These findings demonstrate that the Fully Convolutional DenseNet (FC-enseNet) performs better than other state-of-the-art methods in sea-land segmentation, based on both AIC and BIC. Considering training time efficiency, DeeplabV3+ performs better for sea-land segmentation. The sea-land segmentation benchmark dataset is available at: https://pan.baidu.com/s/1BlnHiltOLbLKe4TG8lZ5xg. Numéro de notice : A2020-220 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01490419.2020.1713266 Date de publication en ligne : 20/01/2020 En ligne : https://doi.org/10.1080/01490419.2020.1713266 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94917 [article]

Double projection planes method for generating enriched disparity maps from multi-view stereo satellite images / Suliman Alaeldin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 11 (November 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 11 (November 2017) . - pp 749 - 760 Titre : Double projection planes method for generating enriched disparity maps from multi-view stereo satellite images Type de document : Article/Communication Auteurs : Suliman Alaeldin, Auteur ; Yun Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 749 - 760 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte [Termes IGN] disparité [Termes IGN] image multi sources [Termes IGN] interpolation [Termes IGN] modèle stéréoscopique [Termes IGN] projection [Termes IGN] zone urbaine Résumé : (auteur) The use of disparity information is computationally coste-effective for 3D-assisted mapping applications. However, off-nadir satellite images over dense urban areas suffer from severe occlusion. Therefore, large gaps will be created due to the unsuccessful matching in the occluded areas. Commonly, gap interpolation produces misleading information that destroys the quality of the subsequent information extraction application. Hence, the more reliable solution is to fill the occlusion gaps by supplementary data. However, disparity maps are the co-relation of one stereo pair. Hence, supplementary disparity maps cannot be directly generated and applied. Thus, this paper introduces the Double Projection Plane (DPP) method for constructing disparity proportionality among multi-view stereo satellite images and calculating analytically the transferring scales required for producing supplementary disparity data. Accordingly, based on multi-view stereo satellite images, this method is promising to provide gap-and-outlier-free disparity maps that have the potential to replace the need for elevation models to some extent. Numéro de notice : A2017-717 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.14358/PERS.83.10.74 En ligne : https://doi.org/10.14358/PERS.83.10.749 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88328 [article]

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Registration-based mapping of aboveground disparities (RMAD) for building detection in off-nadir VHR stereo satellite imagery / Suliman Alaeldin in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 7 (juillet 2016) . - pp 535 - 546 Titre : Registration-based mapping of aboveground disparities (RMAD) for building detection in off-nadir VHR stereo satellite imagery Type de document : Article/Communication Auteurs : Suliman Alaeldin, Auteur ; Yun Zhang, Auteur ; Raid Al-Tahir, Auteur Année de publication : 2016 Article en page(s) : pp 535 - 546 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] détection du bâti [Termes IGN] disparité [Termes IGN] image à très haute résolution [Termes IGN] modèle stéréoscopique [Termes IGN] traitement d'image [Termes IGN] visée oblique Résumé : (Auteur) Reliable building delineation in very high resolution (VHR) satellite imagery can be achieved by precise disparity information extracted from stereo pairs. However, off-nadir VHR images over urban areas contain many occlusions due to building leaning that creates gaps in the extracted disparity maps. The typical approach to fill these gaps is by interpolation. However, it inevitably degrades the quality of the disparity map and reduces the accuracy of building detection. Thus, this research proposes a registration-based technique for mapping the disparity of off-terrain objects to avoid the need for disparity interpolation and normalization. The generated disparity by the proposed technique is then used to support building detection in off-nadir VHR satellite images. Experiments in a high-rise building area confirmed that 75 percent of the detected building roofs overlap precisely the reference data, with almost 100 percent correct detection. These accuracies are substantially higher than those achieved by other published research. Numéro de notice : A2016-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.7.535 En ligne : http://dx.doi.org/10.14358/PERS.82.7.535 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81586 [article]

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RPC-based coregistration of VHR imagery for urban change detection / Shabnam Jabari in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 7 (juillet 2016) . - pp 521 - 534 Titre : RPC-based coregistration of VHR imagery for urban change detection Type de document : Article/Communication Auteurs : Shabnam Jabari, Auteur ; Yun Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 521 - 534 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] angle de visée [Termes IGN] coefficient de corrélation [Termes IGN] détection de changement [Termes IGN] image à très haute résolution [Termes IGN] image Geoeye [Termes IGN] image Ikonos [Termes IGN] image multitemporelle [Termes IGN] image Worldview [Termes IGN] milieu urbain [Termes IGN] modèle numérique de surface [Termes IGN] modèle par fonctions rationnelles [Termes IGN] points homologues Résumé : (Auteur) In urban change detection, coregistration between bi-temporal Very High Resolution (VHR) images taken from different viewing angles, especially from high off-nadir angles, is very challenging. The relief displacements of elevated objects in such images usually lead to significant misregistration that negatively affects the accuracy of change detection. This paper presents a novel solution, called Patch-Wise CoRegistration (PWCR), that can overcome the misregistration problem caused by viewing angle difference and accordingly improve the accuracy of urban change detection. The PWCR method utilizes a Digital Surface Model (DSM) and the Rational Polynomial Coefficients (RPCs) of the images to find corresponding points in a bi-temporal image set. The corresponding points are then used to generate corresponding patches in the image set. To prove that the PWCR method can overcome the misregistration problem and help achieving accurate change detection, two change detection criteria are tested and incorporated into a change detection framework. Experiments on four bi-temporal image sets acquired by Ikonos, GeoEye-1, and Worldview-2 satellites from different viewing angles show that the PWCR method can achieve highly accurate image patch coregistration (up to 80 percent higher than traditional coregistration for elevated objects), so that the change detection framework can produce accurate urban change detection results (over 90 percent). Numéro de notice : A2016-514 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 0.14358/PERS.82.7.521 En ligne : http://dx.doi.org/10.14358/PERS.82.7.521 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81585 [article]

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A combined object- and pixel-based image analysis framework for urban land cover classification of VHR imagery / Bahram Salehi in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

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Sensitivity analysis of various influences on the planimetric displacement of commercial high-resolution satellite imagery / Yun Zhang in Geomatica, vol 54 n° 4 (December 2000)  

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