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Towards global scale segmentation with OpenStreetMap and remote sensing / Munazza Usmani in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 8 (April 2023)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 8 (April 2023) . - n° 100031 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 sol Résumé : (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 [article]

Automatic detection of thin oil films on water surfaces in ultraviolet imagery / Ming Xie in Photogrammetric record, vol 38 n° 181 (March 2023)  

Public [article]  inPhotogrammetric record > vol 38 n° 181 (March 2023) . - pp 47 - 62 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 mer Résumé : (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 [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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 199 - 211 Titre : Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence Type de document : Article/Communication Auteurs : Sidi Wu, Auteur ; Konrad Schindler, Auteur ; Magnus Heitzler, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 199 - 211 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte ancienne [Termes IGN] cartographie historique [Termes IGN] classification dirigée [Termes IGN] détection de changement [Termes IGN] données anciennes [Termes IGN] matrice de co-occurrence [Termes IGN] réseau antagoniste génératif [Termes IGN] segmentation d'image [Termes IGN] vision par ordinateur Résumé : (auteur) Historical maps depict past states of the Earth’s surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence. Numéro de notice : A2023-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2023.01.021 Date de publication en ligne : 14/02/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.01.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102804 [article]

Multi-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)  

Public [article]  inRemote sensing of environment > vol 286 (March 2023) . - n° 113442 Titre : Multi-sensor airborne lidar requires intercalibration for consistent estimation of light attenuation and plant area density Type de document : Article/Communication Auteurs : Grégoire Vincent, Auteur ; Philippe Verley, Auteur ; Benjamin Brede, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 113442 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] canopée [Termes IGN] densité de la végétation [Termes IGN] données lidar [Termes IGN] forêt tropicale [Termes IGN] Guyane (département français) [Termes IGN] image captée par drone [Termes IGN] plan de vol [Termes IGN] rayonnement lumineux [Termes IGN] réflectance végétale [Termes IGN] semis de points [Termes IGN] zone d'intérêt Résumé : (auteur) Leaf area is a key structural characteristic of forest canopies because of the role of leaves in controlling many biological and physical processes occurring at the biosphere-atmosphere transition. High pulse density Airborne Laser Scanning (ALS) holds promise to provide spatially resolved and accurate estimates of plant area density (PAD) in forested landscapes, a key step in understanding forest functioning: phenology, carbon uptake, transpiration, radiative balance etc. Inconsistencies between different ALS sensors is a barrier to generating globally harmonised PAD estimates. The basic assumption on which PAD estimation is based is that light attenuation is proportional to vegetation area density. This study shows that the recorded extinction strongly depends on target detectability which is influenced by laser characteristics (power, sensitivity, wavelength). Three different airborne laser scanners were flown over a wet tropical forest at the Paracou research station in French Guiana. Different sensors, flight heights and transmitted power levels were compared. Light attenuation was retrieved with an open source ray-tracing code (http://amapvox.org). Direct comparison revealed marked differences (up-to 25% difference in profile-averaged light attenuation rate and 50% difference at particular heights) that could only be explained by differences in scanner characteristics. We show how bias which may occur under various acquisition conditions can generally be mitigated by a sensor intercalibration. Alignment of light weight lidar attenuation profiles to ALS reference attenuation profiles is not always satisfactory and we discuss what are the likely sources of discrepancies. Neglecting the dependency of apparent light attenuation on scanner properties may lead to biases in estimated vegetation density commensurate to those affecting light attenuation estimates. Applying intercalibration procedures supports estimation of plant area density independent of acquisition characteristics. Numéro de notice : A2023-169 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113442 Date de publication en ligne : 06/01/2023 En ligne : https://doi.org/10.1016/j.rse.2022.113442 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102928 [article]

Point 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)  

Public [article]  inISPRS International journal of geo-information > vol 12 n° 3 (March 2023) . - n° 90 Titre : Point cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction Type de document : Article/Communication Auteurs : Shuo Shi, Auteur ; Xingtao Tang, Auteur ; Bowen Chen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 90 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse spectrale [Termes IGN] arbre urbain [Termes IGN] détection d'objet [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Houston (Texas) [Termes IGN] interpolation [Termes IGN] réflectance spectrale [Termes IGN] segmentation [Termes IGN] semis de points Résumé : (auteur) Lidar can effectively obtain three-dimensional information on ground objects. In recent years, lidar has developed rapidly from single-wavelength to multispectral hyperspectral imaging. The multispectral airborne lidar Optech Titan is the first commercial system that can collect point cloud data on 1550, 1064, and 532 nm channels. This study proposes a method of point cloud segmentation in the preprocessed intensity interpolation process to solve the problem of inaccurate intensity at the boundary during point cloud interpolation. The entire experiment consists of three steps. First, a multispectral lidar point cloud is obtained using point cloud segmentation and intensity interpolation; the spatial dimension advantage of the multispectral point cloud is used to improve the accuracy of spectral information interpolation. Second, point clouds are divided into eight categories by constructing geometric information, spectral reflectance information, and spectral characteristics. Accuracy evaluation and contribution analysis are also conducted through point cloud truth value and classification results. Lastly, the spatial dimension information is enhanced by point cloud drop sampling, the method is used to solve the error caused by airborne scanning and single-tree extraction of urban trees. Classification results showed that point cloud segmentation before intensity interpolation can effectively improve the interpolation and classification accuracies. The total classification accuracy of the data is improved by 3.7%. Compared with the extraction result (377) of single wood without subsampling treatment, the result of the urban tree extraction proved the effectiveness of the proposed method with a subsampling algorithm in improving the accuracy. Accordingly, the problem of over-segmentation is solved, and the final single-wood extraction result (329) is markedly consistent with the real situation of the region. Numéro de notice : A2023-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12030090 Date de publication en ligne : 23/02/2023 En ligne : https://doi.org/10.3390/ijgi12030090 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102852 [article]

SALT: 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)  

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Siamese 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)  

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A unified attention paradigm for hyperspectral image classification / Qian Liu in IEEE Transactions on geoscience and remote sensing, vol 61 n° 3 (March 2023)  

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PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes / Weixiao Gao in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)  

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Topology-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)  

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Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

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Detection of growth change of young forest based on UAV RGB images at single-tree level / Xiaocheng Zhou in Forests, vol 14 n° 1 (January 2023)  

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Geospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image / Taposh Mollick in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)  

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A hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)  

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Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach / Shenglong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 195 (January 2023)  

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