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A dual-generator translation network fusing texture and structure features for SAR and optical image matching / Han Nie in Remote sensing, Vol 14 n° 12 (June-2 2022)
Titre : A dual-generator translation network fusing texture and structure features for SAR and optical image matching Type de document : Article/Communication Auteurs : Han Nie, Auteur ; Zhitao Fu, Auteur ; Bo-Hui Tang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2946 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] agrégation de détails
[Termes IGN] appariement d'images
[Termes IGN] fusion d'images
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] rapport signal sur bruit
[Termes IGN] rift
[Termes IGN] texture d'imageRésumé : (auteur) The matching problem for heterologous remote sensing images can be simplified to the matching problem for pseudo homologous remote sensing images via image translation to improve the matching performance. Among such applications, the translation of synthetic aperture radar (SAR) and optical images is the current focus of research. However, the existing methods for SAR-to-optical translation have two main drawbacks. First, single generators usually sacrifice either structure or texture features to balance the model performance and complexity, which often results in textural or structural distortion; second, due to large nonlinear radiation distortions (NRDs) in SAR images, there are still visual differences between the pseudo-optical images generated by current generative adversarial networks (GANs) and real optical images. Therefore, we propose a dual-generator translation network for fusing structure and texture features. On the one hand, the proposed network has dual generators, a texture generator, and a structure generator, with good cross-coupling to obtain high-accuracy structure and texture features; on the other hand, frequency-domain and spatial-domain loss functions are introduced to reduce the differences between pseudo-optical images and real optical images. Extensive quantitative and qualitative experiments show that our method achieves state-of-the-art performance on publicly available optical and SAR datasets. Our method improves the peak signal-to-noise ratio (PSNR) by 21.0%, the chromatic feature similarity (FSIMc) by 6.9%, and the structural similarity (SSIM) by 161.7% in terms of the average metric values on all test images compared with the next best results. In addition, we present a before-and-after translation comparison experiment to show that our method improves the average keypoint repeatability by approximately 111.7% and the matching accuracy by approximately 5.25%. Numéro de notice : A2022-562 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14122946 Date de publication en ligne : 20/06/2022 En ligne : https://doi.org/10.3390/rs14122946 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101237
in Remote sensing > Vol 14 n° 12 (June-2 2022) . - n° 2946[article]
Titre : Context-aware network for semantic segmentation toward large-scale point clouds in urban environments Type de document : Article/Communication Auteurs : Chun Liu, Auteur ; Doudou Zeng, Auteur ; Akram Akbar, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5703915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] agrégation de détails
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] graphe
[Termes IGN] prise en compte du contexte
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] zone urbaineRésumé : (auteur) Point cloud semantic segmentation in urban scenes plays a vital role in intelligent city modeling, autonomous driving, and urban planning. Point cloud semantic segmentation based on deep learning methods has achieved significant improvement. However, it is also challenging for accurate semantic segmentation in large scenes due to complex elements, variety of scene classes, occlusions, and noise. Besides, most methods need to split the original point cloud into multiple blocks before processing and cannot directly deal with the point clouds on a large scale. We propose a novel context-aware network (CAN) that can directly deal with large-scale point clouds. In the proposed network, a local feature aggregation module (LFAM) is designed to preserve rich geometric details in the raw point cloud and reduce the information loss during feature extraction. Then, in combination with a global context aggregation module (GCAM), capture long-range dependencies to enhance the network feature representation and suppress the noise. Finally, a context-aware upsampling module (CAUM) is embedded into the proposed network to capture the global perception from a broad perspective. The ensemble of low-level and high-level features facilitates the effectiveness and efficiency of 3-D point cloud feature refinement. Comprehensive experiments were carried out on three large-scale point cloud datasets in both outdoor and indoor environments to evaluate the performance of the proposed network. The results show that the proposed method outperformed the state-of-the-art representative semantic segmentation networks, and the overall accuracy (OA) of Tongji-3D, Semantic3D, and Stanford large-scale 3-D indoor spaces (S3DIS) is 96.01%, 95.0%, and 88.55%, respectively. Numéro de notice : A2022-561 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3182776 Date de publication en ligne : 13/06/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3182776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101188
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 6 (June 2022) . - n° 5703915[article]
Titre : VD-LAB: A view-decoupled network with local-global aggregation bridge for airborne laser scanning point cloud classification Type de document : Article/Communication Auteurs : Jihao Li, Auteur ; Martin Weinmann, Auteur ; Xian Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 19 - 33 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agrégation de détails
[Termes IGN] apprentissage profond
[Termes IGN] précision de la classification
[Termes IGN] qualité du modèle
[Termes IGN] semis de points
[Termes IGN] télémétrie laser aéroportéRésumé : (Auteur) Airborne Laser Scanning (ALS) point cloud classification is a valuable and practical task in the fields of photogrammetry and remote sensing. It takes an extremely important role in many applications of surveying, monitoring, planning, production and living. Recently, driven by the wave of deep learning, the classification of ALS point clouds has also been gradually shifting from traditional feature design to careful deep network architecture construction. Although significant progress has been achieved by leveraging deep learning technology, there are still some matters demanding prompt solution: (1) the coupling phenomenon of high-level semantic features from multiple field-of-views; (2) information propagation without aggregated local–global features in different levels of symmetrical structure; (3) quite serious class-imbalanced distribution problems in large-scale ALS point clouds. In this paper, to tackle these matters, we propose a novel View-Decoupled Network with Local–global Aggregation Bridge (VD-LAB) model. More concretely, a View-Decoupled (VD) grouping method is set at the deepest layer of the network. Then, we establish a Local–global Aggregation Bridge (LAB) between down-sampling path and up-sampling path of the same level. After that, a Self-Amelioration (SA) loss is taken as the optimization objective to train the whole model in an end-to-end manner. Extensive experiments on four challenging ALS point cloud datasets (LASDU, US3D, ISPRS 3D and GML) demonstrate that our VD-LAB is able to achieve state-of-the-art performance in terms of Overall Accuracy (OA) and mean -score (e.g., reaching 88.01% and 78.42% for LASDU dataset, respectively) with very few model parameters and it possesses a strong generalization capability. In addition, the visualization of achieved results also reveals more satisfactory classification for some categories, such as Water in the US3D dataset and Powerline in the ISPRS 3D dataset. Ultimately, the effect of each module in VD-LAB is assessed in detailed ablation analyses as well. Numéro de notice : A2022-067 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.01.012 Date de publication en ligne : 10/02/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.01.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99789
in ISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 19 - 33[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2022041 SL Revue Centre de documentation Revues en salle Disponible 081-2022043 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022042 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt
Titre : Clutter reduction methods for point symbols in map mashups Type de document : Article/Communication Auteurs : Jari Korpi, Auteur ; Paula Ahonen-Rainio, Auteur Année de publication : 2013 Article en page(s) : pp 257 - 265 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] agrégation de détails
[Termes IGN] application composite
[Termes IGN] figuré ponctuel
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] placement automatique des signes conventionnels
[Termes IGN] réductionRésumé : (Auteur) Map mashups are often visually chaotic and methods for solving this chaos are required. We introduce a set of clutter reduction criteria for evaluating methods to reduce clutter in map mashups. We present a synthesis of cartographic generalisation operators for point data and clutter reduction methods used in information visualisation and evaluate the methods against the criteria. The resulting evaluation table can be used in finding suitable clutter reduction methods for cases of map mashups with different primary criteria, and more specifically in finding methods that cover each others’ limitations. Numéro de notice : A2013-465 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1179/1743277413Y.0000000065 En ligne : https://doi.org/10.1179/1743277413Y.0000000065 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32603
in Cartographic journal (the) > vol 50 n° 3 (August 2013) . - pp 257 - 265[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 030-2013031 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : A symmetry detector for map generalization and urban-space analysis Type de document : Article/Communication Auteurs : Jan‐Henrik Haunert, Auteur Année de publication : 2012 Article en page(s) : pp 66 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] agrégation de détails
[Termes IGN] analyse spatiale
[Termes IGN] bati
[Termes IGN] Boston (Massachusetts)
[Termes IGN] données vectorielles
[Termes IGN] empreinte
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] polygone
[Termes IGN] simplification de contour
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) This article presents an algorithmic approach to the problem of finding symmetries in building footprints, which is motivated by map generalization tasks such as symmetry-preserving building simplification and symmetry-aware grouping and aggregation. Moreover, symmetries in building footprints may be used for landmark selection and building classification. The presented method builds up on existing methods for symmetry detection in vector data that use algorithms for string matching. It detects both mirror symmetries and repetitions of geometric structures. In addition to the existing vector-based methods, the new method finds partial symmetries in polygons while allowing for small geometric errors and, based on a least-squares approach, computes optimally adjusted mirror axes and assesses their quality. Finally, the problem of grouping symmetry relations is addressed with an algorithm that finds mirror axes that are almost collinear. The presented approach was tested on a large building dataset of the metropolitan Boston area and its results were compared with results that were manually generated in an empirical test. The symmetry relations that the participants of the test considered most important were found by the algorithm. Future work will deal with the integration of information on symmetry relations into algorithms for map generalization. Numéro de notice : A2012-604 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.08.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.08.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32050
in ISPRS Journal of photogrammetry and remote sensing > vol 74 (Novembrer 2012) . - pp 66 - 77[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2012081 SL Revue Centre de documentation Revues en salle Disponible PermalinkPermalinkPermalinkPermalinkPermalinkPermalink
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