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Application of a hand-held LiDAR scanner for the urban cadastral detail survey in digitized cadastral area of Taiwan urban city / Shih-Hong Chio in Remote sensing, vol 13 n° 24 (December-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 24 (December-2 2021) . - n° 4981 Titre : Application of a hand-held LiDAR scanner for the urban cadastral detail survey in digitized cadastral area of Taiwan urban city Type de document : Article/Communication Auteurs : Shih-Hong Chio, Auteur ; Kai-Wen Hou, Auteur Année de publication : 2021 Article en page(s) : n° 4981 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] cadastre numérique [Termes IGN] chevauchement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] étalonnage [Termes IGN] étude de faisabilité [Termes IGN] lidar mobile [Termes IGN] milieu urbain [Termes IGN] plan cadastral [Termes IGN] Ransac (algorithme) [Termes IGN] semis de points [Termes IGN] Taïwan Résumé : (auteur) The cadastral detail data is used for overlap analysis with digitized graphic cadastral maps to solve the problem of inconsistencies between cadastral maps and the current land situation. This study investigated the feasibility of a handheld LiDAR scanner to collect 3D point clouds in an efficient way for a detail survey in urban environments with narrow and winding streets. Then, urban detail point clouds were collected by the handheld LiDAR scanner. After point cloud filtering and the ranging systematic error correction that was determined by a plane-based calibration method, the collected point clouds were transformed to the TWD97 cadastral coordinate system using control points. The land detail line data were artificially digitized and the results showed that about 97% error of the digitized detail positions was less than 15 cm compared to the check points surveyed by a total station. The results demonstrated the feasibility of using a handheld LiDAR scanner to perform an urban cadastral detail survey in digitized graphic areas. Therefore, the handheld LiDAR scanner could be used for the production of the detail lines for urban cadastral detail surveying for digitized cadastral areas in Taiwan. Numéro de notice : A2021-888 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13244981 Date de publication en ligne : 08/12/2021 En ligne : https://doi.org/10.3390/rs13244981 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99242 [article]

Efficient occluded road extraction from high-resolution remote sensing imagery / Dejun Feng in Remote sensing, vol 13 n° 24 (December-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 24 (December-2 2021) . - n° 4974 Titre : Efficient occluded road extraction from high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Dejun Feng, Auteur ; Xingyu Shen, Auteur ; Yakun Xie, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4974 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] détection de partie cachée [Termes IGN] extraction de traits caractéristiques [Termes IGN] extraction du réseau routier [Termes IGN] image à haute résolution [Termes IGN] reconstruction de route Résumé : (auteur) Road extraction is important for road network renewal, intelligent transportation systems and smart cities. This paper proposes an effective method to improve road extraction accuracy and reconstruct the broken road lines caused by ground occlusion. Firstly, an attention mechanism-based convolution neural network is established to enhance feature extraction capability. By highlighting key areas and restraining interference features, the road extraction accuracy is improved. Secondly, for the common broken road problem in the extraction results, a heuristic method based on connected domain analysis is proposed to reconstruct the road. An experiment is carried out on a benchmark dataset to prove the effectiveness of this method, and the result is compared with that of several famous deep learning models including FCN8s, SegNet, U-Net and D-Linknet. The comparison shows that this model increases the IOU value and the F1 score by 3.35–12.8% and 2.41–9.8%, respectively. Additionally, the result proves the proposed method is effective at extracting roads from occluded areas. Numéro de notice : A2021-889 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13244974 Date de publication en ligne : 07/12/2021 En ligne : https://doi.org/10.3390/rs13244974 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99243 [article]

Adaptive feature weighted fusion nested U-Net with discrete wavelet transform for change detection of high-resolution remote sensing images / Congcong Wang in Remote sensing, vol 13 n° 24 (December-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 24 (December-2 2021) . - n° Titre : Adaptive feature weighted fusion nested U-Net with discrete wavelet transform for change detection of high-resolution remote sensing images Type de document : Article/Communication Auteurs : Congcong Wang, Auteur ; Wenbin Sun, Auteur ; Deqin Fan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] détection de changement [Termes IGN] fusion de données [Termes IGN] image à haute résolution [Termes IGN] pondération [Termes IGN] réseau neuronal siamois [Termes IGN] transformation en ondelettes Résumé : (auteur) The characteristics of a wide variety of scales about objects and complex texture features of high-resolution remote sensing images make deep learning-based change detection methods the mainstream method. However, existing deep learning methods have problems with spatial information loss and insufficient feature representation, resulting in unsatisfactory effects of small objects detection and boundary positioning in high-resolution remote sensing images change detection. To address the problems, a network architecture based on 2-dimensional discrete wavelet transform and adaptive feature weighted fusion is proposed. The proposed network takes Siamese network and Nested U-Net as the backbone; 2-dimensional discrete wavelet transform is used to replace the pooling layer; and the inverse transform is used to replace the upsampling to realize image reconstruction, reduce the loss of spatial information, and fully retain the original image information. In this way, the proposed network can accurately detect changed objects of different scales and reconstruct change maps with clear boundaries. Furthermore, different feature fusion methods of different stages are proposed to fully integrate multi-scale and multi-level features and improve the comprehensive representation ability of features, so as to achieve a more refined change detection effect while reducing pseudo-changes. To verify the effectiveness and advancement of the proposed method, it is compared with seven state-of-the-art methods on two datasets of Lebedev and SenseTime from the three aspects of quantitative analysis, qualitative analysis, and efficiency analysis, and the effectiveness of proposed modules is validated by an ablation study. The results of quantitative analysis and efficiency analysis show that, under the premise of taking into account the operation efficiency, our method can improve the recall while ensuring the detection precision, and realize the improvement of the overall detection performance. Specifically, it shows an average improvement of 37.9% and 12.35% on recall, and 34.76% and 11.88% on F1 with the Lebedev and SenseTime datasets, respectively, compared to other methods. The qualitative analysis shows that our method has better performance on small objects detection and boundary positioning than other methods, and a more refined change map can be obtained. Numéro de notice : A2021-920 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13244971 Date de publication en ligne : 07/12/2021 En ligne : https://doi.org/10.3390/rs13244971 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99244 [article]