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Global-aware siamese network for change detection on remote sensing images / Ruiqian Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 199 (May 2023)
[article]
Titre : Global-aware siamese network for change detection on remote sensing images Type de document : Article/Communication Auteurs : Ruiqian Zhang, Auteur ; Hanchao Zhang, Auteur ; Xiaogang Ning, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 61 - 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de sensibilité
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] détection de changement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) Change detection (CD) in remote sensing images is one of the most important technical options to identify changes in observations in an efficient manner. CD has a wide range of applications, such as land use investigation, urban planning, environmental monitoring and disaster mapping. However, the frequently occurring class imbalance problem brings huge challenges to the change detection applications. To address this issue, we develop a novel global-aware siamese network (GAS-Net), aiming to generate global-aware features for efficient change detection by incorporating the relationships between scenes and foregrounds. The proposed GAS-Net, consisting of the global-attention module (GAM) and foreground-awareness module (FAM) that both learns contextual relationships and enhances symbiotic relation learning between scene and foreground. The experimental results demonstrate the effectiveness and robustness of the proposed GAS-Net, achieving up to 91.21% and 95.84% F1 score on two widely used public datasets, i.e., Levir-CD and Lebedev-CD dataset. The source code is available at https://github.com/xiaoxiangAQ/GAS-Net. Numéro de notice : 2023-204 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2023.04.001 Date de publication en ligne : 05/04/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.04.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103106
in ISPRS Journal of photogrammetry and remote sensing > vol 199 (May 2023) . - pp 61 - 72[article]Automatic generation of outline-based representations of landmark buildings with distinctive shapes / Peng Ti in International journal of geographical information science IJGIS, vol 37 n° 4 (April 2023)
[article]
Titre : Automatic generation of outline-based representations of landmark buildings with distinctive shapes Type de document : Article/Communication Auteurs : Peng Ti, Auteur ; Tao Xiong, Auteur ; Yuhong Qiu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 864 - 884 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Bâti-3D
[Termes IGN] cartographie
[Termes IGN] contour
[Termes IGN] détection du bâti
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] raisonnement spatial
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation d'image
[Termes IGN] sémiologie graphique
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Landmark buildings are salient features for spatial cognition on maps. Distinctive outlines are the major visual characteristics that separate landmark buildings from their surrounding environments. The automatic symbolization of landmark outlines facilitates recognition and map production. As users often recognize landmarks by the outlines of their façades from a street view, this study proposes an automatic method for automatically generating representations of the outlines of landmark buildings in four steps: (1) extract outlines from street-view photographs using GrabCut method, (2) vectorize the extracted building outlines, (3) simplify outline shapes, and (4) symbolize the simplified building outlines in three dimensions (3D). We used the proposed method to generate test data with symbolized outlines for eight buildings in a real-world environment for a wayfinding experiment in which the subjects used the building representations to identify landmark buildings and evaluated their perception of the generated maps. The subjects successfully recognized these buildings based on the symbolized outlines on a map, expressed satisfaction with the manually generated 3D symbols, and reported the same or similar ease of building recognition using 2D or 3D symbolized outlines. Numéro de notice : A2023-207 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2143503 Date de publication en ligne : 11/11/2022 En ligne : https://doi.org/10.1080/13658816.2022.2143503 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103109
in International journal of geographical information science IJGIS > vol 37 n° 4 (April 2023) . - pp 864 - 884[article]Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery / Maryam Hosseini in Computers, Environment and Urban Systems, vol 101 (April 2023)
[article]
Titre : Mapping the walk: A scalable computer vision approach for generating sidewalk network datasets from aerial imagery Type de document : Article/Communication Auteurs : Maryam Hosseini, Auteur ; Andres Sevtsuk, Auteur ; Fabio Miranda, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101950 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'objet
[Termes IGN] Etats-Unis
[Termes IGN] image aérienne
[Termes IGN] navigation pédestre
[Termes IGN] segmentation sémantique
[Termes IGN] système d'information géographique
[Termes IGN] trottoir
[Termes IGN] vision par ordinateurRésumé : (auteur) While cities around the world are increasingly promoting streets and public spaces that prioritize pedestrians over vehicles, significant data gaps have made pedestrian mapping, analysis, and modeling challenging to carry out. Most cities, even in industrialized economies, still lack information about the location and connectivity of their sidewalks, making it difficult to implement research on pedestrian infrastructure and holding the technology industry back from developing accurate, location-based Apps for pedestrians, wheelchair users, street vendors, and other sidewalk users. To address this gap, we have designed and implemented an end-to-end open-source tool— Tile2Net —for extracting sidewalk, crosswalk, and footpath polygons from orthorectified aerial imagery using semantic segmentation. The segmentation model, trained on aerial imagery from Cambridge, MA, Washington DC, and New York City, offers the first open-source scene classification model for pedestrian infrastructure from sub-meter resolution aerial tiles, which can be used to generate planimetric sidewalk data in North American cities. Tile2Net also generates pedestrian networks from the resulting polygons, which can be used to prepare datasets for pedestrian routing applications. The work offers a low-cost and scalable data collection methodology for systematically generating sidewalk network datasets, where orthorectified aerial imagery is available, contributing to over-due efforts to equalize data opportunities for pedestrians, particularly in cities that lack the resources necessary to collect such data using more conventional methods. Numéro de notice : A2023-187 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.compenvurbsys.2023.101950 Date de publication en ligne : 22/02/2023 En ligne : https://doi.org/10.1016/j.compenvurbsys.2023.101950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102961
in Computers, Environment and Urban Systems > vol 101 (April 2023) . - n° 101950[article]Towards global scale segmentation with OpenStreetMap and remote sensing / Munazza Usmani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 8 (April 2023)
[article]
Titre : Towards global scale segmentation with OpenStreetMap and remote sensing Type de document : Article/Communication Auteurs : Munazza Usmani, Auteur ; Maurizio Napolitano, Auteur ; Francesca Bovolo, Auteur Année de publication : 2023 Article en page(s) : n° 100031 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] données localisées des bénévoles
[Termes IGN] image à haute résolution
[Termes IGN] information sémantique
[Termes IGN] occupation du sol
[Termes IGN] OpenStreetMap
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] utilisation du solRésumé : (auteur) Land Use Land Cover (LULC) segmentation is a famous application of remote sensing in an urban environment. Up-to-date and complete data are of major importance in this field. Although with some success, pixel-based segmentation remains challenging because of class variability. Due to the increasing popularity of crowd-sourcing projects, like OpenStreetMap, the need for user-generated content has also increased, providing a new prospect for LULC segmentation. We propose a deep-learning approach to segment objects in high-resolution imagery by using semantic crowdsource information. Due to satellite imagery and crowdsource database complexity, deep learning frameworks perform a significant role. This integration reduces computation and labor costs. Our methods are based on a fully convolutional neural network (CNN) that has been adapted for multi-source data processing. We discuss the use of data augmentation techniques and improvements to the training pipeline. We applied semantic (U-Net) and instance segmentation (Mask R-CNN) methods and, Mask R–CNN showed a significantly higher segmentation accuracy from both qualitative and quantitative viewpoints. The conducted methods reach 91% and 96% overall accuracy in building segmentation and 90% in road segmentation, demonstrating OSM and remote sensing complementarity and potential for city sensing applications. Numéro de notice : A2023-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ophoto.2023.100031 Date de publication en ligne : 16/02/2023 En ligne : https://doi.org/10.1016/j.ophoto.2023.100031 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102807
in ISPRS Open Journal of Photogrammetry and Remote Sensing > vol 8 (April 2023) . - n° 100031[article]Automatic detection of thin oil films on water surfaces in ultraviolet imagery / Ming Xie in Photogrammetric record, vol 38 n° 181 (March 2023)
[article]
Titre : Automatic detection of thin oil films on water surfaces in ultraviolet imagery Type de document : Article/Communication Auteurs : Ming Xie, Auteur ; Xiurui Zhang, Auteur ; Ying Li, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 47 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] détection automatique
[Termes IGN] filtre optique
[Termes IGN] hydrocarbure
[Termes IGN] image AVIRIS
[Termes IGN] marée noire
[Termes IGN] niveau de gris (image)
[Termes IGN] rayonnement ultraviolet
[Termes IGN] segmentation d'image
[Termes IGN] seuillage binaire
[Termes IGN] surface de la merRésumé : (auteur) Among the various remote sensing technologies that have been applied to monitor oil spills on the sea surface, passive ultraviolet (UV) imaging is a controversial one that has raised some disputes in the community of oil spill remote sensing. As a result, the research and applications of oil spill detection using passive UV imaging have not been as developed as other methods. In order to clarify some existing questions on oil spill detection using passive UV remote sensing technology, this paper discusses the needs of thin oil film detection, examines the feasibility of thin oil film detection using passive UV imaging through field experiments under controlled conditions and validates it with the UV imagery derived from the airborne visible/infrared imaging spectrometer (AVIRIS) observation of the Deepwater Horizon oil spill. Two types of fully automatic models are designed to extract the thin oil films on the water surface: (1) a binary classification model based on an adaptive threshold; (2) an unsupervised image segmentation model based on image clustering and greyscale histogram analysis. The two models are tested on the UV imagery obtained through both field experiments and AVIRIS observations. The results indicate that the binary classification model can extract the thin oil films with reasonable accuracy under stable imaging conditions, while the unsupervised image clustering model can robustly detect the thin oil films at the cost of higher computational complexity. These results infer that passive UV imaging is an effective way to detect thin oil films and could be applied to provide early warning at the beginning stage of oil spills and reduce further damage. It may also be applied as a supplementary method for oil spill detection to achieve comprehensive oil spill monitoring. Numéro de notice : A2023-163 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12439 Date de publication en ligne : 09/02/2023 En ligne : https://doi.org/10.1111/phor.12439 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102866
in Photogrammetric record > vol 38 n° 181 (March 2023) . - pp 47 - 62[article]Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence / Sidi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)PermalinkMulti-sensor airborne lidar requires intercalibration for consistent estimation of light attenuation and plant area density / Grégoire Vincent in Remote sensing of environment, vol 286 (March 2023)PermalinkPoint cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction / Shuo Shi in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)PermalinkSALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images / Hao Wu in Computers, Environment and Urban Systems, vol 100 (March 2023)PermalinkSiamese KPConv: 3D multiple change detection from raw point clouds using deep learning / Iris de Gelis in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)PermalinkA unified attention paradigm for hyperspectral image classification / Qian Liu in IEEE Transactions on geoscience and remote sensing, vol 61 n° 3 (March 2023)PermalinkPSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes / Weixiao Gao in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)PermalinkTopology-based individual tree segmentation for automated processing of terrestrial laser scanning point clouds / Xin Xu in International journal of applied Earth observation and geoinformation, vol 116 (February 2023)PermalinkPermalinkCross-supervised learning for cloud detection / Kang Wu in GIScience and remote sensing, vol 60 n° 1 (2023)Permalink