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Region level SAR image classification using deep features and spatial constraints / Anjun Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 163 (May 2020) . - pp 36-48 Titre : Region level SAR image classification using deep features and spatial constraints Type de document : Article/Communication Auteurs : Anjun Zhang, Auteur ; Xuezhi Yang, Auteur ; Shuai Fang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 36-48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] carte de confiance [Termes descripteurs IGN] champ aléatoire de Markov [Termes descripteurs IGN] chatoiement [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] lissage de données [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] superpixel Résumé : (auteur) The region-level SAR image classification algorithms which combine CNN (Convolutional Neural Networks) with super-pixel have been proposed to enhance the classification accuracy compared with the pixel-level algorithms. However, the spatial constraints between the super-pixel regions are not considered, which may limit the performance of these algorithms. To address this problem, an RCC-MRF (RCC, Region Category Confidence-degree) and CNN based region-level SAR image classification algorithm which explores the deep features extracted by CNN and the spatial constraints between super-pixel regions is proposed in this paper. The initial labels of super-pixel regions are obtained using a voting strategy based on the predicted labels CNN. The unary energy function of RCC-MRF is designed to find the category that a region most probably belongs to by using the RCC term which is constructed based on the probability distributions over all categories of pixels predicted by CNN. The binary energy function of RCC-MRF explores the spatial constraints between the adjacent super-pixel regions. In our proposed algorithm, the pixel-level misclassifications can be reduced by the smoothing within regions and the region-level misclassifications will be rectified by minimizing the energy function of RCC-MRF. Experiments have been done on simulated and real SAR images to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm notably outperforms the other CNN-based region-level SAR image classification algorithms. Numéro de notice : A2020-136 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.001 date de publication en ligne : 07/03/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94752 [article]

Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1777 - 1789 Titre : Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints Type de document : Article/Communication Auteurs : Li Liu, Auteur ; Luping Xu, Auteur ; Houzhang Fang, Auteur Année de publication : 2020 Article en page(s) : pp 1777 - 1789 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse bivariée [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] interpolation polynomiale [Termes descripteurs IGN] itération [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] programmation par contraintes [Termes descripteurs IGN] texture d'image Résumé : (Auteur) Infrared (IR) images are often contaminated by obvious intensity bias and stripes, which severely affect the visual quality and subsequent applications. It is challenging to eliminate simultaneously the mixed nonuniformity noise without blurring the fine-image details in low-textured IR images. In this article, we present a new model for simultaneous intensity bias correction and destriping through introducing two sparsity constraints. One is that model fit on the intensity bias should be as accurate as possible. A bivariate polynomial model is built to characterize the global smoothness of the intensity bias. The other constraint is that the unidirectional variational sparse model can concisely represent the direction characteristic of stripe noise. A computationally efficient numerical algorithm based on split Bregman iteration is used to solve the complex optimization problem. The proposed method is fundamentally different from the existing denoising techniques and simultaneously estimates the sharp image, intensity bias, and stripe components. Significant improvement on image quality is achieved on both simulated and real studies. Both qualitative and quantitative comparisons with the state-of-the-art correction methods demonstrate its superiority. Numéro de notice : A2020-089 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2948601 date de publication en ligne : 18/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2948601 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94663 [article]

Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia / Neil Flood in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 15 p. Titre : Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia Type de document : Article/Communication Auteurs : Neil Flood, Auteur ; Fiona Watson, Auteur ; Lisa Collett, Auteur Année de publication : 2019 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre (flore) [Termes descripteurs IGN] bois sur pied [Termes descripteurs IGN] Buisson [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] mosaïque d'images [Termes descripteurs IGN] Queensland (Australie) [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] texture d'image Résumé : (auteur) Convolutional neural networks offer a new approach to classifying high resolution imagery. We use the U-net neural network architecture to map the presence or absence of trees and large shrubs across the Australian state of Queensland. From a state-wide mosaic of 1 m resolution 3-band Earth-i imagery, a selection of 827 squares (1 km2) are manually labeled for the presence of trees or large shrubs, and these are used to train the neural network. The training is intended to capture the textures which are primary visual cues of such vegetation. The trained neural network has an accuracy on independent data of around 90%. The resulting map over the whole of Queensland (1.73 million km2) is intended to be manually checked, and edited where necessary, to provide a high quality map of woody vegetation extent to serve a range of government policy objectives. Numéro de notice : A2019-474 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.101897 date de publication en ligne : 28/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.101897 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93635 [article]

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)  

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Application des algorithmes de Deep learning pour les images SAR / Luc Baudoux (2019) 

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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)  

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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)  

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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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