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SemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images / Daifeng Peng in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5891 - 5906 Titre : SemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images Type de document : Article/Communication Auteurs : Daifeng Peng, Auteur ; Lorenzo Bruzzone, Auteur ; Yongjun Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 5891 - 5906 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bâtiment [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de changement [Termes IGN] entropie [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique Résumé : (auteur) Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of deep learning, most recent developments of CD methods have introduced the use of deep learning techniques to increase the accuracy and automation level over traditional methods. However, when using supervised CD methods, a large amount of labeled data is needed to train deep convolutional networks with millions of parameters. These labeled data are difficult to acquire for CD tasks. To address this limitation, a novel semisupervised convolutional network for CD (SemiCDNet) is proposed based on a generative adversarial network (GAN). First, both the labeled data and unlabeled data are input into the segmentation network to produce initial predictions and entropy maps. Then, to exploit the potential of unlabeled data, two discriminators are adopted to enforce the feature distribution consistency of segmentation maps and entropy maps between the labeled and unlabeled data. During the competitive training, the generator is continuously regularized by utilizing the unlabeled information, thus improving its generalization capability. The effectiveness and reliability of our proposed method are verified on two high-resolution remote sensing data sets. Extensive experimental results demonstrate the superiority of the proposed method against other state-of-the-art approaches. Numéro de notice : A2021-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3011913 Date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3011913 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97986 [article]

Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation / Yansheng Li in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 20 - 33 Titre : Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation Type de document : Article/Communication Auteurs : Yansheng Li, Auteur ; Te Shi, Auteur ; Yongjun Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 20 - 33 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] classification semi-dirigée [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] programmation par contraintes [Termes IGN] segmentation sémantique Résumé : (auteur) Due to its wide applications, remote sensing (RS) image semantic segmentation has attracted increasing research interest in recent years. Benefiting from its hierarchical abstract ability, the deep semantic segmentation network (DSSN) has achieved tremendous success on RS image semantic segmentation and has gradually become the mainstream technology. However, the superior performance of DSSN highly depends on two conditions: (I) massive quantities of labeled training data exist; (II) the testing data seriously resemble the training data. In actual RS applications, it is difficult to fully meet these conditions due to the RS sensor variation and the distinct landscape variation in different geographic locations. To make DSSN fit the actual RS scenario, this paper exploits the cross-domain RS image semantic segmentation task, which means that DSSN is trained on one labeled dataset (i.e., the source domain) but is tested on another varied dataset (i.e., the target domain). In this setting, the performance of DSSN is inevitably very limited due to the data shift between the source and target domains. To reduce the disadvantageous influence of data shift, this paper proposes a novel objective function with multiple weakly-supervised constraints to learn DSSN for cross-domain RS image semantic segmentation. Through carefully examining the characteristics of cross-domain RS image semantic segmentation, multiple weakly-supervised constraints include the weakly-supervised transfer invariant constraint (WTIC), weakly-supervised pseudo-label constraint (WPLC) and weakly-supervised rotation consistency constraint (WRCC). Specifically, DualGAN is recommended to conduct unsupervised style transfer between the source and target domains to carry out WTIC. To make full use of the merits of multiple constraints, this paper presents a dynamic optimization strategy that dynamically adjusts the constraint weights of the objective function during the training process. With full consideration of the characteristics of the cross-domain RS image semantic segmentation task, this paper gives two cross-domain RS image semantic segmentation settings: (I) variation in geographic location and (II) variation in both geographic location and imaging mode. Extensive experiments demonstrate that our proposed method remarkably outperforms the state-of-the-art methods under both of these settings. The collected datasets and evaluation benchmarks have been made publicly available online (https://github.com/te-shi/MUCSS). Numéro de notice : A2021-261 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.009 Date de publication en ligne : 06/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97302 [article]

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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 IGN] analyse de données [Termes IGN] appariement d'images [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] fusion de données multisource [Termes IGN] limite de résolution radiométrique [Termes IGN] modèle numérique de surface [Termes IGN] précision infrapixellaire [Termes 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 Affiliation des auteurs : non IGN 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 Affiliation des auteurs : non IGN 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 IGN] contrôle qualité [Termes IGN] détection d'erreur [Termes IGN] distance orthogonale [Termes IGN] erreur aléatoire [Termes IGN] image à haute résolution [Termes IGN] image SPOT 5 [Termes IGN] modèle par fonctions rationnelles [Termes 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 Affiliation des auteurs : non IGN 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)  

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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)  

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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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