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Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery / Qian Shen in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 78 - 94 Titre : Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Qian Shen, Auteur ; Jiru Huang, Auteur ; Min Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 78 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] données qualitatives [Termes IGN] estimation quantitative [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] jeu de données [Termes IGN] réseau neuronal siamois Résumé : (auteur) In the field of remote sensing applications, semantic change detection (SCD) simultaneously identifies changed areas and their change types by jointly conducting bitemporal image classification and change detection. It facilitates change reasoning and provides more application value than binary change detection (BCD), which offers only a binary map of the changed/unchanged areas. In this study, we propose a multitask Siamese network, named the semantic feature-constrained change detection (SFCCD) network, for building change detection in bitemporal high-spatial-resolution (HSR) images. SFCCD conducts feature extraction, semantic segmentation and change detection simultaneously, where change detection and semantic segmentation are the main and auxiliary tasks, respectively. For the segmentation task, ResNet50 is used to conduct image feature extraction, and the extracted semantic features are provided to execute the change detection task via a series of jump connections. For the change detection task, a global channel attention (GCA) module and a multiscale feature fusion (MSFF) module are designed, where high-level features offer training guidance to the low-level feature maps, and multiscale features are fused with multiple convolutions that possess different receptive fields. In bitemporal HSR images with different view angles, high-rise buildings have different directional height displacements, which generally cause serious false alarms for common change detection methods. However, known public building change detection datasets often lack buildings with height displacement. We thus create the Nanjing Dataset (NJDS) and design the aforementioned network structures and modules to target this issue. Experiments for method validation and comparison are conducted on the NJDS and two additional public datasets, i.e., the WHU Building Dataset (WBDS) and Google Dataset (GDS). Ablation experiments on the NJDS show that the joint utilization of the GCA and MSFF modules performs better than several classic modules, including atrous spatial pyramid pooling (ASPP), efficient spatial pyramid (ESP), channel attention block (CAB) and global attention upsampling (GAU) modules, in dealing with building height displacement. Furthermore, SFCCD achieves higher accuracy in terms of the OA, recall, F1-score and mIoU measures than several state-of-the-art change detection methods, including deeply supervised image fusion network (DSIFN), the dual-task constrained deep Siamese convolutional network (DTCDSCN), and multitask U-Net (MTU-Net). Numéro de notice : A2022-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.05.001 Date de publication en ligne : 12/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.05.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100762 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2022071	SL	Revue	Centre de documentation	Revues en salle	Disponible

Coupling relationship among scale parameter, segmentation accuracy, and classification accuracy in GeOBIA / Ming Dongping in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 11 (November 2018) . - pp 681-693 Titre : Coupling relationship among scale parameter, segmentation accuracy, and classification accuracy in GeOBIA Type de document : Article/Communication Auteurs : Ming Dongping, Auteur ; Wen Zhou, Auteur ; Xu Lu, Auteur ; Min Wang, Auteur ; Yanni Ma, Auteur Année de publication : 2018 Article en page(s) : pp 681-693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] précision de la classification [Termes IGN] segmentation d'image Résumé : (Auteur) The quality of multi-scale segmentation mainly consists of intrasegment homogeneity and intersegment heterogeneity; however, it is difficult to synchronously get both high. It is crucial to make it clear which one of these two measures is more important and what is the coupling relationship among segmentation scale parameter, image segmentation and classification accuracy. This paper employs series of segmentation and classification to show that (1) intrasegment homogeneity is more important than intersegment heterogeneity in GeOBIA; there is always highly positive correlation between intrasegment homogeneity and classification accuracy; (2) with the increase of spectral heterogeneity parameter, both image object amount and the intrasegment homogeneity decrease; however the intersegment heterogeneity increases or increases first then decrease after the appropriate scale; and (3) the appropriate scale means there is a compromise between intrasegment homogeneity and intersegment heterogeneity. The research findings are helpful to raise awareness among practitioners who suffer from scale issues in GeOBIA. Numéro de notice : A2018-484 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.11.681 Date de publication en ligne : 01/11/2018 En ligne : https://doi.org/10.14358/PERS.84.11.681 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91209 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2018111	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Pan-sharpening via deep metric learning / Yinghui Xing in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018) . - pp 165 - 183 Titre : Pan-sharpening via deep metric learning Type de document : Article/Communication Auteurs : Yinghui Xing, Auteur ; Min Wang, Auteur ; Shuyuan Yang, Auteur ; Licheng Jiao, Auteur Année de publication : 2018 Article en page(s) : pp 165 - 183 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] image Quickbird [Termes IGN] image Worldview [Termes IGN] pansharpening (fusion d'images) [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) Neighbors Embedding based pansharpening methods have received increasing interests in recent years. However, image patches do not strictly follow the similar structure in the shallow MultiSpectral (MS) and PANchromatic (PAN) image spaces, consequently leading to a bias to the pansharpening. In this paper, a new deep metric learning method is proposed to learn a refined geometric multi-manifold neighbor embedding, by exploring the hierarchical features of patches via multiple nonlinear deep neural networks. First of all, down-sampled PAN images from different satellites are divided into a large number of training image patches and are then grouped coarsely according to their shallow geometric structures. Afterwards, several Stacked Sparse AutoEncoders (SSAE) with similar structures are separately constructed and trained by these grouped patches. In the fusion, image patches of the source PAN image pass through the networks to extract features for formulating a deep distance metric and thus deriving their geometric labels. Then, patches with the same geometric labels are grouped to form geometric manifolds. Finally, the assumption that MS patches and PAN patches form the same geometric manifolds in two distinct spaces, is cast on geometric groups to formulate geometric multi-manifold embedding for estimating high resolution MS image patches. Some experiments are taken on datasets acquired by different satellites. The experimental results demonstrate that our proposed method can obtain better fusion results than its counterparts in terms of visual results and quantitative evaluations. Numéro de notice : A2018-493 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.016 Date de publication en ligne : 17/02/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91236 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018111	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018113	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018112	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Performance analysis of BDS/GPS precise point positioning with undifferenced ambiguity resolution / Min Wang in Advances in space research, vol 60 n° 12 (15 December 2017)  

Public [article]  inAdvances in space research > vol 60 n° 12 (15 December 2017) . - pp 2581 - 2595 Titre : Performance analysis of BDS/GPS precise point positioning with undifferenced ambiguity resolution Type de document : Article/Communication Auteurs : Min Wang, Auteur ; Hongzhou Chai, Auteur ; Yu Li, Auteur Année de publication : 2017 Article en page(s) : pp 2581 - 2595 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] données BeiDou [Termes IGN] données GPS [Termes IGN] erreur systématique [Termes IGN] méthode des moindres carrés [Termes IGN] positionnement ponctuel précis [Termes IGN] résolution d'ambiguïté Résumé : (auteur) The undifferenced ambiguity resolution has been proved to be an effective method to shorten the initialization of precise point positioning (PPP) solution and improve the positioning accuracy. Several techniques were proposed for undifferenced ambiguity resolution with GPS observations. However, for BeiDou navigation satellite system (BDS), the satellite-induced variation in pseudorange observation changes the characteristics of Melbourne-Wűbbena (MW) combination observation, which leads to unacceptably low fixing rate of undifferenced ambiguity. Besides, the characteristics of satellite-induced variations in BDS observations vary with orbit type of satellite, which should be considered in correction effort. In this paper, the BDS fractional cycle biases (FCBs) are estimated with least-squares estimation method using the float undifferenced ambiguity collected from the network of reference stations. Based on the analysis of weekly stability of widelane FCBs and the distribution of fractional ambiguity parts, it is proven that the satellite-induced variation correction is necessary for the FCB estimation for IGSO and MEO satellites. Contaminated by relatively large orbit error, the ambiguities of GEO satellites should be skipped for ambiguity resolution attempt. Resolving BDS ambiguities in BDS/GPS combined PPP could significantly shorten the time needed for the first correct ambiguity resolution (FCAR). The experiment results of static PPP demonstrate that 90.6% of all sessions accomplish FCAR within 1350 s with only GPS observations. Meanwhile, by adding BDS ambiguities to the subset of ambiguity resolution, 91.9% of all sessions accomplish FCAR with only 870 s. Numéro de notice : A2017-752 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2017.01.045 En ligne : https://doi.org/10.1016/j.asr.2017.01.045 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89037 [article]

Cropland extraction based on OBIA and adaptive scale pre-estimation / Ming Dongping in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 8 (August 2016) . - pp 635 - 644 Titre : Cropland extraction based on OBIA and adaptive scale pre-estimation Type de document : Article/Communication Auteurs : Ming Dongping, Auteur ; Xiang Zhang, Auteur ; Min Wang, Auteur ; Wen Zhou, Auteur Année de publication : 2016 Article en page(s) : pp 635 - 644 Langues : Anglais (eng) Numéro de notice : A2016-609 Affiliation des auteurs : non IGN Nature : Article DOI : 10.14358/PERS.82.8.635 En ligne : http://dx.doi.org/10.14358/PERS.82.8.635 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81809 [article]

Refining high spatial resolution remote sensing image segmentation for man-made objects through acollinear and ipsilateral neighborhood model / Min Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 5 (May 2015)  

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