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Spectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines / Rogério Galante Negri in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 2863 - 2876 Titre : Spectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines Type de document : Article/Communication Auteurs : Rogério Galante Negri, Auteur ; Alejandro C. Frery, Auteur ; Wallace Casaca, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2863 - 2876 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse de sensibilité [Termes IGN] classification non dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection d'ombre [Termes IGN] détection de changement [Termes IGN] détection des nuages [Termes IGN] image ALOS-PALSAR [Termes IGN] image Landsat-OLI [Termes IGN] image Sentinel-MSI [Termes IGN] processus stochastique [Termes IGN] zone homogène Résumé : (auteur) Change detection is a topic of great interest in remote sensing. A good similarity metric to compute the variations among the images is the key to high-quality change detection. However, most existing approaches rely on the fixed threshold values or the user-provided ground truth in order to be effective. The inability to deal with artificial objects such as clouds and shadows is a significant difficulty for many change-detection methods. We propose a new unsupervised change-detection framework to address those critical points. The notion of homogeneous regions is introduced together with a set of geometric operations and statistic-based criteria to characterize and distinguish formally the change and nonchange areas in a pair of remote sensing images. Moreover, a robust and statistically well-posed family of stochastic distances is also proposed, which allows comparing the probability distributions of different regions/objects in the images. These stochastic measures are then used to train a support-vector-machine-based approach in order to detect the change/nonchange areas. Three study cases using the images acquired with different sensors are given in order to compare the proposed method with other well-known unsupervised methods. Numéro de notice : A2021-282 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3009483 date de publication en ligne : 24/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3009483 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97389 [article]

Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery / Yuri Shendryk in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 124 - 136 Titre : Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery Type de document : Article/Communication Auteurs : Yuri Shendryk, Auteur ; Yannik Rist, Auteur ; Catherine Ticehurst, Auteur ; Peter Thorburn, Auteur Année de publication : 2019 Article en page(s) : pp 124 - 136 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Amazonie [Termes IGN] apprentissage profond [Termes IGN] Australie [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'ombre [Termes IGN] état de l'art [Termes IGN] image à haute résolution [Termes IGN] image PlanetScope [Termes IGN] image Sentinel-MSI [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] nuage [Termes IGN] occupation du sol [Termes IGN] zone tropicale humide Résumé : (Auteur) With the increasing availability of high-resolution satellite imagery it is important to improve the efficiency and accuracy of satellite image indexing, retrieval and classification. Furthermore, there is a need for utilizing all available satellite imagery in identifying general land cover types and monitoring their changes through time irrespective of their spatial, spectral, temporal and radiometric resolutions. Therefore, in this study, we developed deep learning models able to efficiently and accurately classify cloud, shadow and land cover scenes in different high-resolution ( Numéro de notice : A2019-494 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.08.018 date de publication en ligne : 17/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.018 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93727 [article]

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Shadow detection and correction using a combined 3D GIS and image processing approach / Safa Ridene in Revue internationale de géomatique, vol 29 n° 3 - 4 (juillet - décembre 2019)  

Public [article]  inRevue internationale de géomatique > vol 29 n° 3 - 4 (juillet - décembre 2019) . - pp 241 - 253 Titre : Shadow detection and correction using a combined 3D GIS and image processing approach Type de document : Article/Communication Auteurs : Safa Ridene, Auteur ; Reda Yaagoubi, Auteur ; Imane Sebari, Auteur ; Audrey Alajouanine, Auteur Année de publication : 2019 Article en page(s) : pp 241 - 253 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection d'ombre [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Matlab [Termes IGN] ModelBuilder [Termes IGN] orthophotographie [Termes IGN] SIG 3D [Termes IGN] Toulouse Résumé : (Auteur) While shadow can give useful information about size and shape of objects, it can pose problems in feature detection and object detection, thereby, it represents one of the major perturbator phenomenons frequently occurring on images and unfortunately, it is inevitable. “Shadows may lead to the failure of image analysis processes and also cause a poor quality of information which in turn leads to problems in implementation of algorithms.” (Mahajan and Bajpayee, 2015). It also affects multiple image analysis applications, whereby shadow cast by buildings deteriorate the spectral values of the surfaces. Therefore, its presence causes a deterioration in the visual image's quality and limits the information that the former could give. Ignoring the existence of shadows in images may cause serious problems in various visual processing applications such as false objects detection. In this context, many researches have been conducted through years. However, it is still a challenge for analysts all over the world to find a fully automated and efficient method for shadow removal from images. Numéro de notice : A2019-640 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3166/rig.2019.00091 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.3166/rig.2019.00091 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95649 [article]

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Diffusion and inpainting of reflectance and height LiDAR orthoimages / Pierre Biasutti in Computer Vision and image understanding, vol 179 (February 2019)    

Public [article]  inComputer Vision and image understanding > vol 179 (February 2019) . - pp 31 - 40 Titre : Diffusion and inpainting of reflectance and height LiDAR orthoimages Type de document : Article/Communication Auteurs : Pierre Biasutti , Auteur ; Jean-François Aujol, Auteur ; Mathieu Brédif , Auteur ; Aurélie Bugeau, Auteur Année de publication : 2019 Projets : SysNum / Article en page(s) : pp 31 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] chaîne de traitement [Termes IGN] convivialité [Termes IGN] densité des points [Termes IGN] détection d'ombre [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] orthoimage [Termes IGN] réflectance [Termes IGN] semis de points Résumé : (Auteur) This paper presents a fully automatic framework for the generation of so-called LiDAR orthoimages (i.e. 2D raster maps of the reflectance and height LiDAR samples) from ground-level LiDAR scans. Beyond the Digital Surface Model (DSM or heightmap) provided by the height orthoimage, the proposed method cost-effectively generates a reflectance channel that is easily interpretable by human operators without relying on any optical acquisition, calibration and registration. Moreover, it commonly achieves very high resolutions (1cm per pixel), thanks to the typical sampling density of static or mobile LiDAR scans. Compared to orthoimages generated from aerial datasets, the proposed LiDAR orthoimages are acquired from the ground level and thus do not suffer occlusions from hovering objects (trees, tunnels and bridges), enabling their use in a number of urban applications such as road network monitoring and management, as well as precise mapping of the public space e.g. for accessibility applications or management of underground networks. Its generation and usability however faces two issues : (i) the inhomogeneous sampling density of LiDAR point clouds and (ii) the presence of masked areas (holes) behind occluders, which include, in a urban context, cars, tree trunks, poles or pedestrians (i) is addressed by first projecting the point cloud on a 2D-pixel grid so as to generate sparse and noisy reflectance and height images from which dense images estimated using a joint anisotropic diffusion of the height and reflectance channels. (ii) LiDAR shadow areas are detected by analyzing the diffusion results so that they can be inpainted using an examplar-based method, guided by an alignment prior. Results on real mobile and static acquisition data demonstrate the effectiveness of the proposed pipeline in generating a very high resolution LiDAR orthoimage of reflectance and height while filling holes of various sizes in a visually satisfying way. Numéro de notice : A2019-168 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cviu.2018.10.011 date de publication en ligne : 24/11/2018 En ligne : https://doi.org/10.1016/j.cviu.2018.10.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92610 [article]

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ICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas / Karine R.M. Adeline in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 311 - 327 Titre : ICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas Type de document : Article/Communication Auteurs : Karine R.M. Adeline, Auteur ; Xavier Briottet , Auteur ; X. Ceamanos, Auteur ; T. Dartigalongue, Auteur ; Jean-Philippe Gastellu-Etchegorry, Auteur Année de publication : 2018 Article en page(s) : pp 311 - 327 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] arbre (flore) [Termes IGN] correction atmosphérique [Termes IGN] détection d'ombre [Termes IGN] houppier [Termes IGN] image à très haute résolution [Termes IGN] image hyperspectrale [Termes IGN] Leaf Area Index [Termes IGN] logiciel de traitement d'image [Termes IGN] modèle de transfert radiatif [Termes IGN] modélisation 3D [Termes IGN] réflectance végétale [Termes IGN] zone urbaine Résumé : (Auteur) Many applications dedicated to urban areas (e.g. land cover mapping and biophysical properties estimation) using high spatial resolution remote sensing images require the use of 3D atmospheric correction methods, able to model complex light interactions within urban topography such as buildings and trees. Currently, one major drawback of these methods is their lack in modeling the radiative signature of trees (e.g. the light transmitted through the tree crown), which leads to an over-estimation of ground reflectance at tree shadows. No study has been carried out to take into account both optical and structural properties of trees in the correction provided by these methods. The aim of this work is to improve an existing 3D atmospheric correction method, ICARE (Inversion Code for urban Areas Reflectance Extraction), to account for trees in its new version, ICARE-VEG (ICARE with VEGetation). After the execution of ICARE, the methodology of ICARE-VEG consists in tree crown delineation and tree shadow detection, and then the application of a physics-based correction factor in order to perform a tree-specific local correction for each pixel in tree shadow. A sensitivity analysis with a design of experiments performed with a 3D canopy radiative transfer code, DART (Discrete Anisotropic Radiative Transfer), results in fixing the two most critical variables contributing to the impact of an isolated tree crown on the radiative energy budget at tree shadow: the solar zenith angle and the tree leaf area index (LAI). Thus, the approach to determine the correction factor relies on an empirical statistical regression and the addition of a geometric scaling factor to account for the tree crown occultation from ground. ICARE-VEG and ICARE performance were compared and validated in the Visible-Near Infrared Region (V-NIR: 0.4–1.0 µm) with hyperspectral airborne data at 0.8 m resolution on three ground materials types, grass, asphalt and water. Results show that (i) ICARE-VEG improves the mean absolute error in retrieved reflectances compared to ICARE in tree shadows by a multiplicative factor ranging between 4.2 and 18.8, and (ii) reduces the spectral bias in reflectance from visible to NIR (due to light transmission through the tree crown) by a multiplicative factor between 1.0 and 1.4 in terms of spectral angle mapper performance. ICARE-VEG opens the way to a complete interpretation of remote sensing images (sunlit, shade cast by both buildings and trees) and the derivation of scientific value-added products over all the entire image without the preliminary step of shadow masking. Numéro de notice : A2018-296 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.015 date de publication en ligne : 01/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90415 [article]

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Self-shadowing of a spacecraft in the computation of surface forces : An example in planetary geodesy / Georges Balmino in Artificial satellites, vol 53 n° 1 (March 2018)  

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Automatic shadow detection in aerial and terrestrial images / Vander Luis de Souza Freitas in Boletim de Ciências Geodésicas, vol 23 n° 4 (oct - dec 2017)    

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An inquiry on contrast enhancement methods for satellite images / Jose-Luis Lisani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

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Satellite images analysis for shadow detection and building height estimation / Gregoris Liasis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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Shadow detection and removal in RGB VHR images for land use unsupervised classification / A. Movia in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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Automatic detection of clouds and shadows using high resolution satellite image time series / Nicolas Champion (2016)  

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Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning / X. Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)

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Shadow detection of man-made buildings in high-resolution panchromatic satellite images / Mohamed I. Elbakary in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)

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Shadow detection in very high spatial resolution aerial images: A comparative study / Karine R.M. Adeline in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)  

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Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery / Xiaohua Tong in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)

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