Centre dedocumentation
scientifique

GANmapper: geographical data translation / Abraham Noah Wu in International journal of geographical information science IJGIS, vol 36 n° 7 (juillet 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 7 (juillet 2022) . - pp 1394 - 1422 Titre : GANmapper: geographical data translation Type de document : Article/Communication Auteurs : Abraham Noah Wu, Auteur ; Filip Biljecki, Auteur Année de publication : 2022 Article en page(s) : pp 1394 - 1422 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] apprentissage automatique [Termes IGN] bâtiment [Termes IGN] distance de Fréchet [Termes IGN] empreinte [Termes IGN] morphologie urbaine [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau routier [Termes IGN] système d'information géographique [Termes IGN] texture d'image Résumé : (auteur) We present a new method to create spatial data using a generative adversarial network (GAN). Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment, bypassing their traditional acquisition techniques (e.g. satellite imagery or land surveying). In the work, we employ land use data and road networks as input to generate building footprints and conduct experiments in 9 cities around the world. The method, which we implement in a tool we release openly, enables the translation of one geospatial dataset to another with high fidelity and morphological accuracy. It may be especially useful in locations missing detailed and high-resolution data and those that are mapped with uncertain or heterogeneous quality, such as much of OpenStreetMap. The quality of the results is influenced by the urban form and scale. In most cases, the experiments suggest promising performance as the method tends to truthfully indicate the locations, amount, and shape of buildings. The work has the potential to support several applications, such as energy, climate, and urban morphology studies in areas previously lacking required data or inpainting geospatial data in regions with incomplete data. Numéro de notice : A2022-493 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2041643 Date de publication en ligne : 08/03/2022 En ligne : https://doi.org/10.1080/13658816.2022.2041643 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100975 [article]

Effective CBIR based on hybrid image features and multilevel approach / D. Latha in Multimedia tools and applications, vol inconnu (March 2022)  

Public [article]  inMultimedia tools and applications > vol inconnu (March 2022) . - pp Titre : Effective CBIR based on hybrid image features and multilevel approach Type de document : Article/Communication Auteurs : D. Latha, Auteur ; A. Geetha, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] base de données d'images [Termes IGN] écart type [Termes IGN] espace colorimétrique [Termes IGN] image en couleur [Termes IGN] image RVB [Termes IGN] matrice de co-occurrence [Termes IGN] motif binaire local [Termes IGN] niveau de gris (image) [Termes IGN] observation multiniveaux [Termes IGN] recherche d'image basée sur le contenu [Termes IGN] saturation de la couleur [Termes IGN] texture d'image [Termes IGN] transformation intensité-teinte-saturation Résumé : (auteur) Content based image retrieval (CBIR) process can retrieve images by matching its feature set values. The proposed novel CBIR methodology called Effective CBIR based on hybrid image features and multilevel approach (CBIR_LTP_GLCM) integrates the hybrid features such as color features and texture features, along with multilevel approach. The color features such as mean and standard deviation are adopted in the proposed method to represent the global color properties of an image. This method manipulates the color input-image by processing the Hue, Saturation and Value channels of the HSV color space. This novel work is enriched with the image feature derived from Local Ternary Pattern (LTP) in addition with GLCM. So, the proposed method CBIR_LTP_GLCM is potentially charged with meaningful modifications travelling with color image manipulation and extended image retrieval accuracy with the aid of multilevel approach. The proposed methodology is experimentally compared with the existing recent CBIR versions by using the standard database such as Corel-1 k, and a user contributed database named DB_VEG. Numéro de notice : A2022-291 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11042-022-12588-7 Date de publication en ligne : 30/03/2022 En ligne : https://doi.org/10.1007/s11042-022-12588-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100337 [article]

Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images / Chen Zheng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10555 - 10574 Titre : Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images Type de document : Article/Communication Auteurs : Chen Zheng, Auteur ; Yun Zhang, Auteur ; Leiguang Wang, Auteur Année de publication : 2021 Article en page(s) : pp 10555 - 10574 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] champ aléatoire de Markov [Termes IGN] granularité d'image [Termes IGN] segmentation sémantique [Termes IGN] texture d'image Résumé : (auteur) Semantic segmentation is one of the most important tasks in remote sensing. However, as spatial resolution increases, distinguishing the homogeneity of each land class and the heterogeneity between different land classes are challenging. The Markov random field model (MRF) is a widely used method for semantic segmentation due to its effective spatial context description. To improve segmentation accuracy, some MRF-based methods extract more image information by constructing the probability graph with pixel or object granularity units, and some other methods interpret the image from different semantic perspectives by building multilayer semantic classes. However, these MRF-based methods fail to capture the relationship between different granularity features extracted from the image and hierarchical semantic classes that need to be interpreted. In this article, a new MRF-based method is proposed to incorporate the multigranularity information and the multilayer semantic classes together for semantic segmentation of remote sensing images. The proposed method develops a framework that builds a hybrid probability graph on both pixel and object granularities and defines a multiclass-layer label field with hierarchical semantic over the hybrid probability graph. A generative alternating granularity inference is suggested to provide the result by iteratively passing and updating information between different granularities and hierarchical semantics. The proposed method is tested on texture images, different remote sensing images obtained by the SPOT5, Gaofen-2, GeoEye, and aerial sensors, and Pavia University hyperspectral image. Experiments demonstrate that the proposed method shows a better segmentation performance than other state-of-the-art methods. Numéro de notice : A2021-873 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3033293 Date de publication en ligne : 11/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3033293 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99132 [article]

Multi-objective CNN-based algorithm for SAR despeckling / Sergio Vitale in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 11 (November 2021) . - pp 9336 - 9349 Titre : Multi-objective CNN-based algorithm for SAR despeckling Type de document : Article/Communication Auteurs : Sergio Vitale, Auteur ; Giampaolo Ferraioli, Auteur ; Vito Pascazio, Auteur Année de publication : 2021 Article en page(s) : pp 9336 - 9349 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage profond [Termes IGN] chatoiement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] filtre de déchatoiement [Termes IGN] image radar moirée [Termes IGN] image Radarsat [Termes IGN] restauration d'image Résumé : (auteur) Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used in applications, such as change detection, image restoration, segmentation, detection, and classification. With reference to the synthetic aperture radar (SAR) domain, the application of DL techniques is not straightforward due to the nontrivial interpretation of SAR images, especially caused by the presence of speckle. Several DL solutions for SAR despeckling have been proposed in the last few years. Most of these solutions focus on the definition of different network architectures with similar cost functions, not involving SAR image properties. In this article, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed. This is achieved by the definition of a peculiar loss function obtained by the weighted combination of three different terms. Each of these terms is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties, and strong scatterers identification. Their combination allows balancing these effects. Moreover, a specifically designed architecture is proposed to effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared with the state-of-art despeckling algorithms, both from a quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous, and extremely heterogeneous. Numéro de notice : A2021-810 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3034852 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3034852 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98874 [article]

Semi-automatic extraction of rural roads under the constraint of combined geometric and texture features / Hai Tan in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 11 (November 2021) . - pp 754 Titre : Semi-automatic extraction of rural roads under the constraint of combined geometric and texture features Type de document : Article/Communication Auteurs : Hai Tan, Auteur ; Zimo Shen, Auteur ; Jiguang Dai, Auteur Année de publication : 2021 Article en page(s) : pp 754 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] chemin rural [Termes IGN] Chine [Termes IGN] coefficient de corrélation [Termes IGN] contrainte géométrique [Termes IGN] corrélation croisée normalisée [Termes IGN] courbure [Termes IGN] extraction de traits caractéristiques [Termes IGN] extraction semi-automatique [Termes IGN] image à haute résolution [Termes IGN] modèle de simulation [Termes IGN] niveau de gris (image) [Termes IGN] route [Termes IGN] texture d'image Résumé : (auteur) The extraction of road information from high-resolution remotely-sensed images has important application value in many fields. Rural roads have the characteristics of relatively narrow widths and diversified pavement materials; these characteristics can easily lead to problems involving the similarity of the road texture with the texture of surrounding objects and make it difficult to improve the automation of traditional high-precision road extraction methods. Based on this background, a semi-automatic rural road extraction method constrained by a combination of geometric and texture features is proposed in this paper. First, an adaptive road width extraction model is proposed to improve the accuracy of the initial road centre point. Then, aiming at the continuous change of curvature of rural roads, a tracking direction prediction model is proposed. Finally, a matching model under geometric texture constraints is proposed, which solves the problem of similarity between road and neighbourhood texture to a certain extent. The experimental results show that by selecting different types of experimental scenes or remotely sensed image data, compared with other methods, the proposed method can not only guarantee the road extraction accuracy but also improve the degree of automation to a certain extent. Numéro de notice : A2021-850 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10110754 Date de publication en ligne : 09/11/2021 En ligne : https://doi.org/10.3390/ijgi10110754 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99009 [article]

Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images / Zhenjiang Wu in Remote sensing, vol 13 n° 20 (October-2 2021)  

Permalink

A feature based change detection approach using multi-scale orientation for multi-temporal SAR images / R. Vijaya Geetha in European journal of remote sensing, vol 54 sup 2 (2021)  

Permalink

Digital camera calibration for cultural heritage documentation: the case study of a mass digitization project of religious monuments in Cyprus / Evagoras Evagorou in European journal of remote sensing, vol 54 sup 1 (2021)  

Permalink

Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

Permalink

Fast unsupervised multi-scale characterization of urban landscapes based on Earth observation data / Claire Teillet in Remote sensing, vol 13 n° 12 (June-2 2021)  

Permalink

Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])  

Permalink

Structure-aware completion of photogrammetric meshes in urban road environment / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Permalink

Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

Permalink

Extraction of impervious surface using Sentinel-1A time-series coherence images with the aid of a Sentinel-2A image / Wenfu Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)  

Permalink

SAR image speckle reduction based on nonconvex hybrid total variation model / Yuli Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Permalink