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Albedo estimation for real-time 3D reconstruction using RGB-D and IR data / Patrick Stotko in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 150 (April 2019) . - pp 213 - 225 Titre : Albedo estimation for real-time 3D reconstruction using RGB-D and IR data Type de document : Article/Communication Auteurs : Patrick Stotko, Auteur ; Michael Weinmann, Auteur ; Reinhard Klein, Auteur Année de publication : 2019 Article en page(s) : pp 213 - 225 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] albedo [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] longueur d'onde [Termes descripteurs IGN] méthode de réduction d'énergie [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] réflectance [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] temps réel [Termes descripteurs IGN] texture d'image Résumé : (Auteur) Reconstructing scenes in real-time using low-cost sensors has gained increasing attention in recent research and enabled numerous applications in graphics, vision, and robotics. While current techniques offer a substantial improvement regarding the quality of the reconstructed geometry, the degree of realism of the overall appearance is still lacking as the reconstruction of accurate surface appearance is highly challenging due to the complex interplay of surface geometry, reflectance properties and surrounding illumination. We present a novel approach that allows the reconstruction of both the geometry and the spatially varying surface albedo of a scene from RGB-D and IR data obtained via commodity sensors. In comparison to previous approaches, our approach offers an improved robustness and a significant speed-up to even fulfill the real-time requirements. For this purpose, we exploit the benefits of scene segmentation to improve albedo estimation due to the resulting better segment-wise coupling of IR and RGB data that takes into account the wavelength characteristics of different materials within the scene. The estimated albedo is directly integrated into the dense volumetric reconstruction framework using a novel weighting scheme to generate high-quality results. In our evaluation, we demonstrate that our approach allows albedo capturing of complicated scenarios including complex, high-frequent and strongly varying lighting as well as shadows. Numéro de notice : A2019-141 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.018 date de publication en ligne : 04/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.018 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92479 [article]

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Method for an automatic alignment of imagery and vector data applied to cadastral information in Poland / Juan J. Ruiz-Lendínez in Survey review, vol 51 n° 365 (March 2019)  

Public [article]  inSurvey review > vol 51 n° 365 (March 2019) . - pp 123 - 134 Titre : Method for an automatic alignment of imagery and vector data applied to cadastral information in Poland Type de document : Article/Communication Auteurs : Juan J. Ruiz-Lendínez, Auteur ; B. Maćkiewicz, Auteur ; P. Motek, Auteur ; T. Stryjakiewicz, Auteur Année de publication : 2019 Article en page(s) : pp 123 - 134 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] carrefour [Termes descripteurs IGN] conflation [Termes descripteurs IGN] données cadastrales [Termes descripteurs IGN] données vectorielles [Termes descripteurs IGN] incertitude géométrique [Termes descripteurs IGN] limite cadastrale [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] Pologne [Termes descripteurs IGN] Poznan [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] texture d'image Résumé : (Auteur) Nowadays, an important problem in combining vector data and imagery is that they rarely align. This problem can become particularly acute in the case of cadastral systems. In this study, and as part of the partnership between the Universities of Jaén and Adam Mickiewicz (Poznań), we provide a methodological proposal to assess the conflation procedures between cadastral vector data and imagery, improving the alignment between both data sets. To do this, we use an automatic alignment algorithm which detects road intersections from both data sets as control points by using image texture characterisation. With this method, we first train the system on the imagery to learn the road texture distribution, then we can obtain its segmentation according to its texture, and finally the system locates road intersection points. The last step is to align vector data and imagery by using different techniques. This algorithm is based on an earlier one, detailed in [Ruiz, J.J., Rubio, T.J., and Ureña, M.A., 2011b. Automatic extraction of road intersections from images in conflation processes based on texture characterization. Survey review, 43 (321), 212–225.]. However, in the updated version we have solved the problem of not-well-defined intersection points, resulting in a substantial increase in the number of intersection points employed for the final adjustment to align both products and in a reduction of the computation time. On the other hand, the positional uncertainty assessment of parcel boundary lines both before and after applying our alignment procedure between them is provided. With regard to the experimental results, in the case of Polish cadastral data this procedure allows for significant improvement in the alignment between imagery and cadastral parcels boundaries. Numéro de notice : A2019-189 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2017.1388959 date de publication en ligne : 20/10/2017 En ligne : https://doi.org/10.1080/00396265.2017.1388959 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92626 [article]

Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 119 - 131 Titre : Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis Type de document : Article/Communication Auteurs : Matheus Pinheiro Ferreira, Auteur ; Fabien Hubert Wagner, Auteur ; Luiz E.O.C. Aragão, Auteur Année de publication : 2019 Article en page(s) : pp 119 - 131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse texturale [Termes descripteurs IGN] arbre (flore) [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] forêt tropicale [Termes descripteurs IGN] houppier [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] matrice de co-occurence [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] variation saisonnière Résumé : (Auteur) Tropical forest conservation and management can significantly benefit from information about the spatial distribution of tree species. Very-high resolution (VHR) spaceborne platforms have been hailed as a promising technology for mapping tree species over broad spatial extents. WorldView-3, the most advanced VHR sensor, provides spectral data in 16 bands covering the visible to near-infrared (VNIR, 400–1040 nm) and shortwave-infrared (SWIR, 1210–2365 nm) wavelength ranges. It also collects images at unprecedented levels of details using a panchromatic band with 0.3-m of spatial resolution. However, the potential of WorldView-3 at its full spectral and spatial resolution for tropical tree species classification remains unknown. In this study, we performed a comprehensive assessment of WorldView-3 images acquired in the dry and wet seasons for tree species discrimination in tropical semi-deciduous forests. Classification experiments were performed using VNIR individually and combined with SWIR channels. To take advantage of the sub-metric resolution of the panchromatic band for classification, we applied an individual tree crown (ITC)-based approach that employed pan-sharpened VNIR bands and gray level co-occurrence matrix texture features. We determined whether the combination of images from the two annual seasons improves the classification accuracy. Finally, we investigated which plant traits influenced species detection. The new SWIR sensing capabilities of WorldView-3 increased the average producer’s accuracy up to 7.8%, by enabling the detection of non-photosynthetic vegetation within ITCs. The combination of VNIR bands from the two annual seasons did not improve the classification results when compared to the results obtained using images from each season individually. The use of VNIR bands at their original 1.2-m spatial resolution yielded average producer’s accuracies of 43.1 ± 3.1% and 38.8 ± 3% in the wet and dry seasons, respectively. The ITC-based approach improved the accuracy to 70 ± 8% in the wet and 68.4 ± 7.4% in the dry season. Texture analysis of the panchromatic band enabled the detection of species-specific differences in crown structure, which improved species detection. The use of texture analysis, pan-sharpening, and ITC delineation is a potential approach to perform tree species classification in tropical forests with WorldView-3 satellite images. Numéro de notice : A2019-117 Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.019 date de publication en ligne : 28/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92444 [article]

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Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)  

Public [article]  inForests > vol 9 n° 10 (October 2018) Titre : Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery Type de document : Article/Communication Auteurs : Lin Chen, Auteur ; Chunying Ren, Auteur ; Bai Zhang, Auteur ; Zongming Wang, Auteur ; Yanbiao Xi, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] Changbai, massif du [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] montagne [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] surveillance forestière [Termes descripteurs IGN] texture d'image [Termes descripteurs IGN] variable biophysique (végétation) Résumé : (Auteur) Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched Sentinel imagery offers a new opportunity for forest AGB mapping and monitoring. In this study, texture characteristics and backscatter coefficients of Sentinel-1, in addition to multispectral bands, vegetation indices, and biophysical variables of Sentinal-2, based on 56 measured AGB samples in the center of the Changbai Mountains, China, were used to develop biomass prediction models through geographically weighted regression (GWR) and machine learning (ML) algorithms, such as the artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that texture characteristics and vegetation biophysical variables were the most important predictors. SVR was the best method for predicting and mapping the patterns of AGB in the study site with limited samples, whose mean error, mean absolute error, root mean square error, and correlation coefficient were 4 × 10−3, 0.07, 0.08 Mg·ha−1, and 1, respectively. Predicted values of AGB from four models ranged from 11.80 to 324.12 Mg·ha−1, and those for broadleaved deciduous forests were the most accurate, while those for AGB above 160 Mg·ha−1 were the least accurate. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using the freely accessible and high-resolution Sentinel imagery, based on ML techniques. Numéro de notice : A2018-478 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100582 date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.3390/f9100582 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91180 [article]

Robust detection and affine rectification of planar homogeneous texture for scene understanding / Shahzor Ahmad in International journal of computer vision, vol 126 n° 8 (August 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 8 (August 2018) . - pp 822 - 854 Titre : Robust detection and affine rectification of planar homogeneous texture for scene understanding Type de document : Article/Communication Auteurs : Shahzor Ahmad, Auteur ; Loong-Fah Cheong, Auteur Année de publication : 2018 Article en page(s) : pp 822 - 854 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] scène [Termes descripteurs IGN] texture d'image [Termes descripteurs IGN] transformation affine Résumé : (Auteur) Man-made environments tend to be abundant with planar homogeneous texture, which manifests as regularly repeating scene elements along a plane. In this work, we propose to exploit such structure to facilitate high-level scene understanding. By robustly fitting a texture projection model to optimal dominant frequency estimates in image patches, we arrive at a projective-invariant method to localize such generic, semantically meaningful regions in multi-planar scenes. The recovered projective parameters also allow an affine-ambiguous rectification in real-world images marred with outliers, room clutter, and photometric severities. Comprehensive qualitative and quantitative evaluations are performed that show our method outperforms existing representative work for both rectification and detection. The potential of homogeneous texture for two scene understanding tasks is then explored. Firstly, in environments where vanishing points cannot be reliably detected, or the Manhattan assumption is not satisfied, homogeneous texture detected by the proposed approach is shown to provide alternative cues to obtain a scene geometric layout. Second, low-level feature descriptors extracted upon affine rectification of detected texture are found to be not only class-discriminative but also complementary to features without rectification, improving recognition performance on the 67-category MIT benchmark of indoor scenes. One of our configurations involving deep ConvNet features outperforms most current state-of-the-art work on this dataset, achieving a classification accuracy of 76.90%. The approach is additionally validated on a set of 31 categories (mostly outdoor man-made environments exhibiting regular, repeating structure), being a subset of the large-scale Places2 scene dataset. Numéro de notice : A2018-415 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1078-2 date de publication en ligne : 22/03/2018 En ligne : https://doi.org/10.1007/s11263-018-1078-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90898 [article]

Quality assessment in point feature generalization with pattern preserved / Wenhao Yu in Transactions in GIS, vol 22 n° 3 (June 2018)  

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Large scale textured mesh reconstruction from mobile mapping images and LIDAR scans / Mohamed Boussaha in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2 ([28/05/2018])  

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Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification / Rama Rao Nidamanuri in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

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An (almost) automated process to track the Martians dunes : ac.GetPreciseShifts / Arthur Coqué (2018) 

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Multiple cues-based active contours for target contour tracking under sophisticated background / Peng Lv in The Visual Computer, vol 33 n°9 (September 2017)  

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Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)  

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Cartographic continuum rendering based on color and texture interpolation to enhance photo-realism perception / Charlotte Hoarau in ISPRS Journal of photogrammetry and remote sensing, vol 127 (May 2017)  

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Assessment of textural differentiations in forest resources in Romania using fractal analysis / Ion Andronache in Forests, vol 8 n° 3 (March 2017)  

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New point matching algorithm using sparse representation of image patch feature for SAR image registration / Jianwei Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

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Urban slum detection using texture and spatial metrics derived from satellite imagery / Divyani Kohli in Journal of spatial science, vol 61 n° 2 (December 2016)  

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