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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]

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True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points / Mojdeh Ebrahimikia in Photogrammetric record, vol 37 n° 178 (June 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 178 (June 2022) . - pp 161 - 184 Titre : True orthophoto generation based on unmanned aerial vehicle images using reconstructed edge points Type de document : Article/Communication Auteurs : Mojdeh Ebrahimikia, Auteur ; Ali Hosseininaveh, Auteur Année de publication : 2022 Article en page(s) : pp 161 - 184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie [Termes IGN] apprentissage profond [Termes IGN] détection de contours [Termes IGN] détection du bâti [Termes IGN] distorsion d'image [Termes IGN] graphe [Termes IGN] image captée par drone [Termes IGN] modèle numérique de surface [Termes IGN] orthophotographie [Termes IGN] orthophotoplan numérique [Termes IGN] photogrammétrie aérienne [Termes IGN] pixel de contour [Termes IGN] structure-from-motion [Termes IGN] zone urbaine Résumé : (auteur) After considering state-of-the-art algorithms, this paper presents a novel method for generating true orthophotos from unmanned aerial vehicle (UAV) images of urban areas. The procedure consists of four steps: 2D edge detection in building regions, 3D edge graph generation, digital surface model (DSM) modification and, finally, true orthophoto and orthomosaic generation. The main contribution of this paper is concerned with the first two steps, in which deep-learning approaches are used to identify the structural edges of the buildings and the estimated 3D edge points are added to the point cloud for DSM modification. Running the proposed method as well as four state-of-the-art methods on two different datasets demonstrates that the proposed method outperforms the existing orthophoto improvement methods by up to 50% in the first dataset and by 70% in the second dataset by reducing true orthophoto distortion in the structured edges of the buildings. Numéro de notice : A2022-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12409 Date de publication en ligne : 05/04/2022 En ligne : https://doi.org/10.1111/phor.12409 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101065 [article]

Application oriented quality evaluation of Gaofen-7 optical stereo satellite imagery / Jiaojiao Tian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-1-2022 (2022 edition) . - pp 145 - 152 Titre : Application oriented quality evaluation of Gaofen-7 optical stereo satellite imagery Type de document : Article/Communication Auteurs : Jiaojiao Tian, Auteur ; Xiangyu Zhuo, Auteur ; Xiangtian Yuan, Auteur ; Corentin Henry, Auteur ; Pablo d' Angelo, Auteur ; Thomas Krauss, Auteur Année de publication : 2022 Article en page(s) : pp 145 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Allemagne [Termes IGN] détection du bâti [Termes IGN] extraction du réseau routier [Termes IGN] image Gaofen [Termes IGN] image optique [Termes IGN] orientation d'image [Termes IGN] reconstruction 3D [Termes IGN] scène urbaine [Termes IGN] segmentation Résumé : (auteur) GaoFen-7 (GF-7) satellite mission is further expanding the very high resolution 3D mapping application. Carrying the first civilian Chinese sub-meter resolution stereo satellite sensors, GF-7 satellite was launched on November 7, 2019. With 0.65 meter resolution on backward view and 0.8 meter resolution forward view, GF-7 has been designed to meet the demand of natural resource monitoring, land surveying, and other mapping applications in China. The use of GF-7 for 3D city reconstruction is unfortunately restricted by the fixed large stereo view angle of forward and backward cameras with +26 and −5 degrees respectively which is not optimal for dense stereo matching in urban regions. In this paper, we intensively evaluate the quality of the GF-7 datasets by performing a series of urban monitoring applications, including road detection, building extraction and 3D reconstruction. In addition, we propose a 3D reconstruction workflow which uses the land cover classification result to refine the stereo matching result. Six sub-urban regions are selected from the available datasets in the middle of Germany. The results show that basic elements in urban scenes like buildings and roads could be detected from GF-7 datasets with high accuracy. With the proposed workflow, a 3D city model with a visually observed good quality can be delivered. Numéro de notice : A2022-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-1-2022-145-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-1-2022-145-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100776 [article]

K-means clustering based on omnivariance attribute for building detection from airborne lidar data / Renato César Dos santos in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 111 - 118 Titre : K-means clustering based on omnivariance attribute for building detection from airborne lidar data Type de document : Article/Communication Auteurs : Renato César Dos santos, Auteur ; Mauricio Galo, Auteur ; A.F. Habib, Auteur Année de publication : 2022 Article en page(s) : pp 111 - 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par nuées dynamiques [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] morphologie mathématique Résumé : (auteur) Building detection is an important process in urban applications. In the last decades, 3D point clouds derived from airborne LiDAR have been widely explored. In this paper, we propose a building detection method based on K-means clustering and the omnivariance attribute derived from eigenvalues. The main contributions lie on the automatic detection without the need for training and optimal neighborhood definition for local attribute estimation. Additionally, one refinement step based on mathematical morphology (MM) operators to minimize the classification errors (commission and omission errors) is proposed. The experiments were conducted in three study areas. In general, the results indicated the potential of proposed method, presenting an average Fscore around 97%. Numéro de notice : A2022-431 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-111-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-111-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100737 [article]

A context feature enhancement network for building extraction from high-resolution remote sensing imagery / Jinzhi Chen in Remote sensing, vol 14 n° 9 (May-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 9 (May-1 2022) . - n° 2276 Titre : A context feature enhancement network for building extraction from high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Jinzhi Chen, Auteur ; Dejun Zhang, Auteur ; Yiqi Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de contours [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] structure-from-motion Résumé : (auteur) The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation by using deep neural networks in building extraction tasks. Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing. We propose a novel fully convolutional neural network called the Context Feature Enhancement Network (CFENet) to address these issues. CFENet comprises three modules: the spatial fusion module, the focus enhancement module, and the feature decoder module. First, the spatial fusion module aggregates the spatial information of low-level features to obtain buildings’ outline and edge information. Secondly, the focus enhancement module fully aggregates the semantic information of high-level features to filter the information of building-related attribute categories. Finally, the feature decoder module decodes the output of the above two modules to segment the buildings more accurately. In a series of experiments on the WHU Building Dataset and the Massachusetts Building Dataset, our CFENet balances efficiency and accuracy compared to the other four methods we compared, and achieves optimality on all five evaluation metrics: PA, PC, F1, IoU, and FWIoU. This indicates that CFENet can effectively enhance and fuse buildings’ low-level and high-level features, improving building extraction accuracy. Numéro de notice : A2022-385 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14092276 Date de publication en ligne : 09/05/2022 En ligne : https://doi.org/10.3390/rs14092276 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100663 [article]

Revising cadastral data on land boundaries using deep learning in image-based mapping / Bujar Fetai in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)  

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Automatic extraction of building geometries based on centroid clustering and contour analysis on oblique images taken by unmanned aerial vehicles / Leilei Zhang in International journal of geographical information science IJGIS, vol 36 n° 3 (March 2022)  

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Building footprint extraction in Yangon city from monocular optical satellite image using deep learning / Hein Thura Aung in Geocarto international, vol 37 n° 3 ([01/03/2022])  

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Comparaison des images satellite et aériennes dans le domaine de la détection d’obstacles à la navigation aérienne et de leur mise à jour / Olivier de Joinville in XYZ, n° 170 (mars 2022)

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Multi-species individual tree segmentation and identification based on improved mask R-CNN and UAV imagery in mixed forests / Chong Zhang in Remote sensing, vol 14 n° 4 (February-2 2022)  

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A combination of convolutional and graph neural networks for regularized road surface extraction / Jingjing Yan in IEEE Transactions on geoscience and remote sensing, vol 60 n° 2 (February 2022)  

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PCEDNet: a lightweight neural network for fast and interactive edge detection in 3D point clouds / Chems-Eddine Himeur in ACM Transactions on Graphics, TOG, Vol 41 n° 1 (February 2022)  

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Three-Dimensional point cloud analysis for building seismic damage information / Fan Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 2 (February 2022)  

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Linear building pattern recognition in topographical maps combining convex polygon decomposition / Zhiwei Wei in Geocarto international, vol 37 n° inconnu ([25/01/2022])  

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Automatic extraction of damaged houses by earthquake based on improved YOLOv5: A case study in Yangbi / Yafei Jing in Remote sensing, vol 14 n° 2 (January-2 2022)  

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