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Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery / Yiping Gong in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 34 - 44 Titre : Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery Type de document : Article/Communication Auteurs : Yiping Gong, Auteur ; Zhifeng Xiao, Auteur ; Xiaowei Tan, Auteur Année de publication : 2020 Article en page(s) : pp 34 - 44 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse contextuelle [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] description multiniveau [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] vision par ordinateur [Termes descripteurs IGN] zone d'intérêt Résumé : (auteur) Object detection in very-high-resolution (VHR) remote sensing imagery remains a challenge. Environmental factors, such as illumination intensity and weather, reduce image quality, resulting in poor feature representation and limited detection accuracy. To enrich the feature representation and mine the underlying context information among objects, this article proposes a context-aware convolutional neural network (CA-CNN) model for object detection that includes proposal generation, context feature extraction, feature fusion, and classification. During feature extraction, we propose integrating a context-regions-of-interests (Context-RoIs) mining layer into the CNN model and extracting context features by mapping Context-RoIs mined from the foreground proposals to multilevel feature maps. Finally, the context features extracted from multilevel layers are fused into a single layer, and the proposals represented by the fused features are classified by a softmax classifier. In this article, through numerous experiments, we thoroughly explore the influence of key factors, such as Context-RoIs, different feature scales, and different spatial context window sizes. Because of the end-to-end network design approach, our proposed model simultaneously maintains high efficiency and effectiveness. We conducted all model testing on the public NWPU VHR-10 data set. The experimental results demonstrate that our proposed CA-CNN model achieves significantly improved model performance and better detection results compared with the state-of-the-art methods. Numéro de notice : A2020-038 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2930246 date de publication en ligne : 23/09/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2930246 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94492 [article]

Georeferenced measurements of building objects with their simultaneous shape detection / Edward Osada in Survey review, Vol 52 n°370 (Janvier 2020)  

Public [article]  inSurvey review > Vol 52 n°370 (Janvier 2020) . - pp 24 - 30 Titre : Georeferenced measurements of building objects with their simultaneous shape detection Type de document : Article/Communication Auteurs : Edward Osada, Auteur ; Krzysztof Karsznia, Auteur ; Izabela Karsznia, Auteur Année de publication : 2020 Article en page(s) : pp 24 - 30 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie [Termes descripteurs IGN] ajustement de paramètres [Termes descripteurs IGN] bâtiment [Termes descripteurs IGN] compensation par moindres carrés [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] information géographique [Termes descripteurs IGN] mesurage d'angles [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] point de nivellement [Termes descripteurs IGN] point GNSS nivelé [Termes descripteurs IGN] reconnaissance de formes Résumé : (auteur) The paper presents a new method of georeferenced measurements of building objects with their simultaneous shape detection. The method is based on a robust adjustment of a linear network localised on the measured object and connected to GNSS-surveyed points deployed near building corners. In the robust adjustment process, the angles of the object physically outlying from their theoretical design values are detected, the remaining angles retain the design values. The performed field surveying experiment underlined the effectiveness of the proposed method. Numéro de notice : A2020-025 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1511033 date de publication en ligne : 17/08/2018 En ligne : https://doi.org/10.1080/00396265.2018.1511033 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94473 [article]

Context pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang 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 201 - 215 Titre : Context pyramidal network for stereo matching regularized by disparity gradients Type de document : Article/Communication Auteurs : Junhua Kang, Auteur ; Lin Chen, Auteur ; Fei Deng, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 201 - 215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] appariement de formes [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] gradient [Termes descripteurs IGN] vision par ordinateur [Termes descripteurs IGN] vision stéréoscopique Résumé : (Auteur) Also after many years of research, stereo matching remains to be a challenging task in photogrammetry and computer vision. Recent work has achieved great progress by formulating dense stereo matching as a pixel-wise learning task to be resolved with a deep convolutional neural network (CNN). However, most estimation methods, including traditional and deep learning approaches, still have difficulty to handle real-world challenging scenarios, especially those including large depth discontinuity and low texture areas. To tackle these problems, we investigate a recently proposed end-to-end disparity learning network, DispNet (Mayer et al., 2015), and improve it to yield better results in these problematic areas. The improvements consist of three major contributions. First, we use dilated convolutions to develop a context pyramidal feature extraction module. A dilated convolution expands the receptive field of view when extracting features, and aggregates more contextual information, which allows our network to be more robust in weakly textured areas. Second, we construct the matching cost volume with patch-based correlation to handle larger disparities. We also modify the basic encoder-decoder module to regress detailed disparity images with full resolution. Third, instead of using post-processing steps to impose smoothness in the presence of depth discontinuities, we incorporate disparity gradient information as a gradient regularizer into the loss function to preserve local structure details in large depth discontinuity areas. We evaluate our model in terms of end-point-error on several challenging stereo datasets including Scene Flow, Sintel and KITTI. Experimental results demonstrate that our model decreases the estimation error compared with DispNet on most datasets (e.g. we obtain an improvement of 46% on Sintel) and estimates better structure-preserving disparity maps. Moreover, our proposal also achieves competitive performance compared to other methods. Numéro de notice : A2019-496 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.012 date de publication en ligne : 27/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93729 [article]

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Residences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])  

Public [article]  inGeocarto international > vol 34 n° 14 [30/10/2019] . - pp 1621 - 1633 Titre : Residences information extraction from Landsat imagery using the multi-parameter decision tree method Type de document : Article/Communication Auteurs : Yujie Yang, Auteur ; Shijie Wang, Auteur ; Xiaoyong Bai, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1621 - 1633 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] albedo [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] classification par arbre de décision [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] eau [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] ombre [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] seuillage d'image Résumé : (auteur) The rapid and accurate grasp of changes in residences is crucial for urban planning and urbanisation. However, the traditional methods for extracting residences exists several problems, which lead to inaccurate extraction results. In this study, the Landsat image is used to establish a new method for extracting the residences quickly and accurately. The specific steps are as follows: (1) We calculate surface albedo to exclude the interference of waters and shadows; (2) Using single-band threshold method, we eliminate the interference of shadows; (3) Normalized Difference Vegetation Index is calculated to exclude the effects of vegetation; (4) Roads are removed by calculating the shape index. Verification shows that the accuracy of this extraction method is 92.81%, which is more accurate than the traditional methods and solves the problems existed in the traditional methods. This novel method is a new reference for other land cover research on the technical aspect. Numéro de notice : A2019-528 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1494760 date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1494760 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94106 [article]

Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data / Emanuele Santi in Remote sensing, Vol 11 n° 20 (2 October 2019)  

Public [article]  inRemote sensing > Vol 11 n° 20 (2 October 2019) . - 18 p. Titre : Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data Type de document : Article/Communication Auteurs : Emanuele Santi, Auteur ; Mohammed Dabboor, Auteur ; Simone Pettinato, Auteur ; Simonetta Paloscia, Auteur Année de publication : 2019 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bande C [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] humidité du sol [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Radarsat [Termes descripteurs IGN] Manitoba (Canada) [Termes descripteurs IGN] polarimétrie [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surface cultivée Résumé : (auteur) This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (O°) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than O°, with correlation coe?cients up to R ' 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of O° and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized O°. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9. Numéro de notice : A2019-555 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11202451 date de publication en ligne : 22/10/2019 En ligne : https://doi.org/10.3390/rs11202451 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94210 [article]

Accurate detection of built-up areas from high-resolution remote sensing imagery using a fully convolutional network / Yihua Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)  

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Saliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

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Delineation of vacant building land using orthophoto and lidar data object classification / Dejan Jenko in Geodetski vestnik, vol 63 n° 3 (September - November 2019)  

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Integration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain / Yolanda Torres in International journal of applied Earth observation and geoinformation, vol 81 (September 2019)  

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Improving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours / David Griffiths in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

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A novel method for separating woody and herbaceous time series / Qiang Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)  

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A cognitive framework for road detection from high-resolution satellite images / Naveen Chandra in Geocarto international, vol 34 n° 8 ([15/06/2019])  

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Automatisation du traitement de données "mobile mapping" : extraction d'éléments linéaires et ponctuels / Loïc Elsholz in XYZ, n° 159 (juin 2019)  

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Polarimétrie radar complète et partielle pour le suivi des surfaces terrestres / Pierre-Louis Frison in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)

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Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network / Jianfeng Huang in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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