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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 descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] littoral [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs 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 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 descripteurs IGN] carte [Termes descripteurs IGN] disparité [Termes descripteurs IGN] image multi sources [Termes descripteurs IGN] interpolation [Termes descripteurs IGN] modèle stéréoscopique [Termes descripteurs IGN] projection [Termes descripteurs 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 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 descripteurs IGN] détection du bâti [Termes descripteurs IGN] disparité [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] modèle stéréoscopique [Termes descripteurs IGN] traitement d'image [Termes descripteurs 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 Thématique : IMAGERIE Nature : Article 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 descripteurs IGN] angle de visée [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image Geoeye [Termes descripteurs IGN] image Ikonos [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modèle par fonctions rationnelles [Termes descripteurs 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 Thématique : IMAGERIE Nature : Article 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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 999 - 1014 Titre : A combined object- and pixel-based image analysis framework for urban land cover classification of VHR imagery Type de document : Article/Communication Auteurs : Bahram Salehi, Auteur ; Yun Zhang, Auteur ; Ming Zhong, Auteur Année de publication : 2013 Article en page(s) : pp 999 - 1014 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image Ikonos [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] Nouveau-Brunswick [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] transformation en ondelettes Résumé : (Auteur) This paper aims at exploiting the advantages of pixel-based and object-based image analysis approaches for urban land cover classification of very high resolution ( VHR ) satellite imagery through a combined object- and pixel-based image analysis framework. The framework starts with segmenting the image resulting in several spectral and spatial features of segments. To overcome the curse of dimensionality, a wavelet- based feature extraction method is proposed to reduce the number of features. The wavelet-based method is automatic, fast, and can preserve local variations in objects' spectral/ spatial signatures. Finally, the extracted features together with the original bands of the image are classified using the conventional pixel-based Maximum Likelihood classification. The proposed method was tested on the WorldView-2, QuickBird, and Ikonos images of the same urban area for comparison purposes. Results show up to 17 percent, 10 percent, and 11 percent improvement in kappa coefficients compared to the case in which only the original bands of the image are used for WV - 2 , QB , and IK , respectively. Furthermore, the objects' spectral features contribute more to increasing classification accuracy than spatial features. Numéro de notice : A2013-596 Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.999 En ligne : https://doi.org/10.14358/PERS.79.11.999 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32732 [article]

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