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A CNN-based subpixel level DSM generation approach via single image super-resolution / Yongjun Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 10 (October 2019) . - pp 765 - 775 Titre : A CNN-based subpixel level DSM generation approach via single image super-resolution Type de document : Article/Communication Auteurs : Yongjun Zhang, Auteur ; Zhi Zheng, Auteur ; Yimin Luo, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 765 - 775 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] fusion de données multisource [Termes descripteurs IGN] limite de résolution radiométrique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] précision infrapixellaire [Termes descripteurs IGN] reconstruction d'image Résumé : (Auteur) Previous work for subpixel level Digital Surface Model (DSM) generation mainly focused on data fusion techniques, which are extremely limited by the difficulty of multisource data acquisition. Although several DSM super resolution (SR) methods have been developed to ease the problem, a new issue that plenty of DSM samples are needed to train the model is raised. Therefore, considering the original images have vital influence on its DSM's accuracy, we address the problem by directly improving images resolution. Several SR models are refined and brought into the traditional DSM generation process as an image quality improvement stage to construct an easy but effective workflow for subpixel level DSM generation. Experiments verified the validity and significance of bringing SR technology into this kind of application. Statistical analysis also confirmed that a subpixel level DSM with higher fidelity can be obtained more easily compared to directly DSM interpolation. Numéro de notice : A2019-524 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.10.765 date de publication en ligne : 01/10/2019 En ligne : https://doi.org/10.14358/PERS.85.10.765 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93997 [article]

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Large-scale remote sensing image retrieval by deep hashing neural networks / Yansheng Li in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 2 (February 2018) . - pp 950 - 965 Titre : Large-scale remote sensing image retrieval by deep hashing neural networks Type de document : Article/Communication Auteurs : Yansheng Li, Auteur ; Yongjun Zhang, Auteur ; Xin Huang, Auteur ; Hu Zhu, Auteur ; Jiayi Ma, Auteur Année de publication : 2018 Article en page(s) : pp 950 - 965 Note générale : Bibliographie Langues : Anglais (eng) Résumé : (Auteur) As one of the most challenging tasks of remote sensing big data mining, large-scale remote sensing image retrieval has attracted increasing attention from researchers. Existing large-scale remote sensing image retrieval approaches are generally implemented by using hashing learning methods, which take handcrafted features as inputs and map the high-dimensional feature vector to the low-dimensional binary feature vector to reduce feature-searching complexity levels. As a means of applying the merits of deep learning, this paper proposes a novel large-scale remote sensing image retrieval approach based on deep hashing neural networks (DHNNs). More specifically, DHNNs are composed of deep feature learning neural networks and hashing learning neural networks and can be optimized in an end-to-end manner. Rather than requiring to dedicate expertise and effort to the design of feature descriptors, we can automatically learn good feature extraction operations and feature hashing mapping under the supervision of labeled samples. To broaden the application field, DHNNs are evaluated under two representative remote sensing cases: scarce and sufficient labeled samples. To make up for a lack of labeled samples, DHNNs can be trained via transfer learning for the former case. For the latter case, DHNNs can be trained via supervised learning from scratch with the aid of a vast number of labeled samples. Extensive experiments on one public remote sensing image data set with a limited number of labeled samples and on another public data set with plenty of labeled samples show that the proposed remote sensing image retrieval approach based on DHNNs can remarkably outperform state-of-the-art methods under both of the examined conditions. Numéro de notice : A2018-192 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2756911 date de publication en ligne : 13/10/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2756911 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89857 [article]

A Stepwise-Then-Orthogonal Regression (STOR) with quality control for optimizing the RFM of high-resolution satellite imagery / Chang Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 9 (September 2017) . - pp 611 - 620 Titre : A Stepwise-Then-Orthogonal Regression (STOR) with quality control for optimizing the RFM of high-resolution satellite imagery Type de document : Article/Communication Auteurs : Chang Li, Auteur ; Non-répertorié, Auteur ; Yongjun Zhang, Auteur ; Zuxun Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 611 - 620 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] contrôle qualité [Termes descripteurs IGN] détection d'erreur [Termes descripteurs IGN] distance orthogonale [Termes descripteurs IGN] erreur aléatoire [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image SPOT 5 [Termes descripteurs IGN] modèle par fonctions rationnelles [Termes descripteurs IGN] régression Résumé : (auteur) There are two major problems in Rational Function Model (RFM) solution: (a) Data source error, including gross error, random error, and systematic error; and (b) Model error, including over-parameterization and over-correction issues caused by unnecessary RFM parameters and exaggeration of random error in constant term of error-in-variables (EIV) model, respectively. In order to solve two major problems simultaneously, we propose a new approach named stepwise-then-orthogonal regression (STOR) with quality control. First, RFM parameters are selected by stepwise regression with gross error detection. Second, the revised orthogonal distance regression is utilized to adjust random error and address the overcorrection problem. Third, systematic error is compensated by Fourier series. The performance of conventional strategies and the proposed STOR are evaluated by control and check grids generated from SPOT5 high-resolution imagery. Compared with the least squares regression, partial least squares regression, ridge regression, and stepwise regression, the proposed STOR shows a significant improvement in accuracy. Numéro de notice : A2017-598 Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.9.611 En ligne : https://doi.org/10.14358/PERS.83.9.611 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86874 [article]

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3D building roof reconstruction from airborne LiDAR point clouds : a framework based on a spatial database / Rujun Cao in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 7-8 (July - August 2017) . - pp 1359 - 1380 Titre : 3D building roof reconstruction from airborne LiDAR point clouds : a framework based on a spatial database Type de document : Article/Communication Auteurs : Rujun Cao, Auteur ; Yongjun Zhang, Auteur ; Xinyi Liu, Auteur ; Zongze Zhao, Auteur Année de publication : 2017 Article en page(s) : pp 1359 - 1380 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] base de données localisées [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] niveau de détail [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] toit Résumé : (Auteur) Three-dimensional (3D) building models are essential for 3D Geographic Information Systems and play an important role in various urban management applications. Although several light detection and ranging (LiDAR) data-based reconstruction approaches have made significant advances toward the fully automatic generation of 3D building models, the process is still tedious and time-consuming, especially for massive point clouds. This paper introduces a new framework that utilizes a spatial database to achieve high performance via parallel computation for fully automatic 3D building roof reconstruction from airborne LiDAR data. The framework integrates data-driven and model-driven methods to produce building roof models of the primary structure with detailed features. The framework is composed of five major components: (1) a density-based clustering algorithm to segment individual buildings, (2) an improved boundary-tracing algorithm, (3) a hybrid method for segmenting planar patches that selects seed points in parameter space and grows the regions in spatial space, (4) a boundary regularization approach that considers outliers and (5) a method for reconstructing the topological and geometrical information of building roofs using the intersections of planar patches. The entire process is based on a spatial database, which has the following advantages: (a) managing and querying data efficiently, especially for millions of LiDAR points, (b) utilizing the spatial analysis functions provided by the system, reducing tedious and time-consuming computation, and (c) using parallel computing while reconstructing 3D building roof models, improving performance. Numéro de notice : A2017-305 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1301456 En ligne : http://dx.doi.org/10.1080/13658816.2017.1301456 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85352 [article]

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Automatic keyline recognition and 3D reconstruction for quasi-planar façades in close-range images / Chang Li in Photogrammetric record, vol 31 n° 153 (March - May 2016)  

Public [article]  inPhotogrammetric record > vol 31 n° 153 (March - May 2016) . - pp. 29 - 50 Titre : Automatic keyline recognition and 3D reconstruction for quasi-planar façades in close-range images Type de document : Article/Communication Auteurs : Chang Li, Auteur ; Yongjun Zhang, Auteur ; Zuxun Zhang, Auteur Année de publication : 2016 Article en page(s) : pp. 29 - 50 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre [Termes descripteurs IGN] appariement de lignes [Termes descripteurs IGN] espace convexe [Termes descripteurs IGN] façade [Termes descripteurs IGN] photogrammétrie métrologique [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] reconstruction 3D du bâti [Termes descripteurs IGN] surface concave Résumé : (Auteur) Critical keylines, such as concave and convex edges of a building façade, can be lost in photogrammetric recognition procedures. To solve this problem and to reconstruct quasi-planar 3D façades automatically and precisely, a set of algorithms and techniques for the automatic recognition of lines and 3D reconstruction is proposed. This includes: (1) a procedure for line-segment matching that satisfies the spatial requirements of a 3D scene based on “global independence” and “local dependence”; (2) a technique of generalised point bundle block adjustment combined with spatial line constraints (in the form of virtual observations) to control the propagation of error; and (3) the methods of perceptual organisation, plane fitting and plane–plane intersection are suggested to acquire the critical keylines corresponding to concave and convex building edges. Experimental results show that these new algorithms are feasible and applicable to recognition and 3D reconstruction. Recommendations for recognition methods are provided depending on whether or not a priori topological relationships are available between the planes under consideration. Numéro de notice : A2016-160 Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12141 date de publication en ligne : 14/03/2016 En ligne : https://doi.org/10.1111/phor.12141 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80460 [article]

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DEM-assisted RFM block adjustment of pushbroom nadir viewing HRS imagery / Yongjun Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

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LiDAR strip adjustment using multifeatures matched with aerial images / Yongjun Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

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Fully automatic generation of geoinformation products with chinese zy-3 satellite imagery / Yongjun Zhang in Photogrammetric record, vol 29 n° 148 (December 2014 - February 2015)  

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Direct georeferencing of airborne LiDAR data in national coordinates / Yongjun Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 84 (October 2013)

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Combined bundle block adjustment with spaceborne linear array and airborne frame array imagery / Yongjun Zhang in Photogrammetric record, vol 28 n° 142 (June - August 2013)  

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