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A comparative study on deep-learning methods for dense image matching of multi-angle and multi-date remote sensing stereo-images / Hessah Albanwan in Photogrammetric record, vol 37 n° 180 (December 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 180 (December 2022) . - pp 385 - 409 Titre : A comparative study on deep-learning methods for dense image matching of multi-angle and multi-date remote sensing stereo-images Type de document : Article/Communication Auteurs : Hessah Albanwan, Auteur ; Rongjun Qin, Auteur Année de publication : 2022 Article en page(s) : pp 385 - 409 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] appariement d'images [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couple stéréoscopique [Termes IGN] modèle stéréoscopique [Termes IGN] précision géométrique (imagerie) Résumé : (auteur) Deep-learning (DL) stereomatching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo-images lacking a systematic evaluation on how robust DL methods are on satellite stereo-images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereomatching methods through hundreds of multi-date multi-site satellite stereopairs with varying geometric configurations, against the traditional well-practiced Census-semi-global matching (SGM), to comprehensively understand their accuracy, robustness, generalisation capabilities, and their practical potential. The DL methods include a learning-based cost metric through convolutional neural networks (MC-CNN) followed by SGM, and three end-to-end (E2E) learning models using Geometry and Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of geometric accuracies, while may not generalise well for unseen data. The learning-based cost metric and Census-SGM are rather robust and can consistently achieve acceptable results. All DL algorithms are robust to geometric configurations of stereopairs and are less sensitive in comparison to the Census-SGM, while learning-based cost metrics can generalise on satellite images when trained on different datasets (airborne or ground-view). Numéro de notice : A2022-938 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12430 Date de publication en ligne : 09/11/2022 En ligne : https://doi.org/10.1111/phor.12430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102684 [article]

Encoder-decoder structure with multiscale receptive field block for unsupervised depth estimation from monocular video / Songnan Chen in Remote sensing, Vol 14 n° 12 (June-2 2022)  

Public [article]  inRemote sensing > Vol 14 n° 12 (June-2 2022) . - n° 2906 Titre : Encoder-decoder structure with multiscale receptive field block for unsupervised depth estimation from monocular video Type de document : Article/Communication Auteurs : Songnan Chen, Auteur ; Junyu Han, Auteur ; Mengxia Tang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2906 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage non-dirigé [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couple stéréoscopique [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] image isolée [Termes IGN] optimisation (mathématiques) [Termes IGN] profondeur [Termes IGN] séquence d'images [Termes IGN] structure-from-motion Résumé : (auteur) Monocular depth estimation is a fundamental yet challenging task in computer vision as depth information will be lost when 3D scenes are mapped to 2D images. Although deep learning-based methods have led to considerable improvements for this task in a single image, most existing approaches still fail to overcome this limitation. Supervised learning methods model depth estimation as a regression problem and, as a result, require large amounts of ground truth depth data for training in actual scenarios. Unsupervised learning methods treat depth estimation as the synthesis of a new disparity map, which means that rectified stereo image pairs need to be used as the training dataset. Aiming to solve such problem, we present an encoder-decoder based framework, which infers depth maps from monocular video snippets in an unsupervised manner. First, we design an unsupervised learning scheme for the monocular depth estimation task based on the basic principles of structure from motion (SfM) and it only uses adjacent video clips rather than paired training data as supervision. Second, our method predicts two confidence masks to improve the robustness of the depth estimation model to avoid the occlusion problem. Finally, we leverage the largest scale and minimum depth loss instead of the multiscale and average loss to improve the accuracy of depth estimation. The experimental results on the benchmark KITTI dataset for depth estimation show that our method outperforms competing unsupervised methods. Numéro de notice : A2022-563 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14122906 En ligne : https://doi.org/10.3390/rs14122906 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101240 [article]

The polar epipolar rectification / François Darmon in IPOL Journal, Image Processing On Line, vol 11 (2021)  

Public [article]  inIPOL Journal, Image Processing On Line > vol 11 (2021) . - pp 56 - 75 Titre : The polar epipolar rectification Type de document : Article/Communication Auteurs : François Darmon, Auteur ; Pascal Monasse, Auteur Année de publication : 2021 Article en page(s) : pp 56 - 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] couple stéréoscopique [Termes IGN] disparité [Termes IGN] géométrie épipolaire [Termes IGN] orthorectification [Termes IGN] points homologues Résumé : (auteur) Epipolar rectification of a stereo pair is the process of resampling a pair of stereo images so that the apparent motion of corresponding points is horizontal. This is an important preliminary step in depth estimation, substituting depth by disparity estimation. Most methods rely on a perspective transform of both images, which has the advantage to simulate a different attitude of the pinhole cameras. A limitation is that when an epipole is inside the image domain, it has to be sent to infinity by the perspective transform, producing a strong distortion. On the contrary, relying on a polar transform centered at the epipole provides a method applicable universally to a pair of pinhole camera views. We present in detail the algorithm, filling in the information important for its implementation and missing in published articles. Numéro de notice : A2021-782 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5201/ipol.2021.328 Date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.5201/ipol.2021.328 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98937 [article]

Aleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis / Max Mehltretter in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 63 - 75 Titre : Aleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis Type de document : Article/Communication Auteurs : Max Mehltretter, Auteur ; Christian Heipke, Auteur Année de publication : 2021 Article en page(s) : pp 63 - 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] corrélation épipolaire dense [Termes IGN] couple stéréoscopique [Termes IGN] courbe épipolaire [Termes IGN] disparité [Termes IGN] effet de profondeur cinétique [Termes IGN] image RVB [Termes IGN] modèle d'incertitude [Termes IGN] modèle stochastique [Termes IGN] voxel Résumé : (auteur) Motivated by the need to identify erroneous disparity estimates, various methods for the estimation of aleatoric uncertainty in the context of dense stereo matching have been presented in recent years. Especially, the introduction of deep learning based methods and the accompanying significant improvement in accuracy have greatly increased the popularity of this field. Despite this remarkable development, most of these methods rely on features learned from disparity maps only, neglecting the corresponding 3-dimensional cost volumes. However, conventional hand-crafted methods have already demonstrated that the additional information contained in such cost volumes are beneficial for the task of uncertainty estimation. In this paper, we combine the advantages of deep learning and cost volume based features and present a new Convolutional Neural Network (CNN) architecture to directly learn features for the task of aleatoric uncertainty estimation from volumetric 3D data. Furthermore, we discuss and apply three different uncertainty models to train our CNN without the need to provide ground truth for uncertainty. In an extensive evaluation on three datasets using three common dense stereo matching methods, we investigate the effects of these uncertainty models and demonstrate the generality and state-of-the-art accuracy of the proposed method. Numéro de notice : A2021-012 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.003 Date de publication en ligne : 18/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96415 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2021011	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2021013	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2021012	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / Roholah Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 18 - 35 Titre : Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation Type de document : Article/Communication Auteurs : Roholah Yazdan, Auteur ; Masood Varshosaz, Auteur Année de publication : 2021 Article en page(s) : pp 18 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] base de données d'images [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] corrélation à l'aide de traits caractéristiques [Termes IGN] corrélation croisée normalisée [Termes IGN] couple stéréoscopique [Termes IGN] détection automatique [Termes IGN] modèle stéréoscopique [Termes IGN] reconnaissance d'objets [Termes IGN] segmentation d'image [Termes IGN] SIFT (algorithme) [Termes IGN] signalisation routière [Termes IGN] SURF (algorithme) [Termes IGN] Téhéran [Termes IGN] transformation de Hough [Termes IGN] zone urbaine Résumé : (auteur) Automatic detection and recognition of traffic signs have many applications. However, some problems can affect the accuracy of the existing algorithms, such as changes in environmental light conditions, shadows, the presence of objects of the same colour, significant changes in scale and rotation, as well as obstacles in front of the traffic signs. To overcome these difficulties, a reference image database is usually used that includes different modes of appearing the traffic signs in the images. In order to overcome the effects of scale and rotation, in this paper a new method is presented in which only one reference image is needed for each sign to recognise the traffic sign in an image. In the proposed method, imaging is done in stereo. Using the captured image pair, a virtual image is generated which is then used to recognise the sign. As a result, the recognition is carried out with a minimum number of reference images. Experiments show that the proposed algorithm significantly improves recognition results. The traffic signs are recognised with 93.1% accuracy that enjoys a 4.9% improvement over traditional methods. Numéro de notice : A2021-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.003 Date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96304 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2021011	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2021013	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2021012	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Epipolar rectification of a generic camera / Marc Pierrot-Deseilligny (2020)    

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Learning stereo reconstruction with deep neural networks / Stepan Tulyakov (2020)  

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Underwater calibration in near real time: Focus on detection optimized by AI and selection of calibration patterns / Loïca Avanthey (2020)  

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CNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

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Towards automatic SAR-optical stereogrammetry over urban areas using very high resolution imagery / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

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Estudio de precision en la aerotriangulacion de bloques de imagenes obtenidas con UAV / Miguel Angel Lopez Gonzalez in Mapping : Teledetección, medio ambiante, cartografía, sistemas de información geográfica, vol 26 n° 185 (septembrie - octubre 2017)    

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Quantitative estimation and validation of the effects of the convergence, bisector elevation, and asymmetry angles on the positioning accuracies of satellite stereo pairs / Jaehoon Jeong in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)  

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Acquisition et reconstruction de données 3D denses sous-marines en eau peu profonde par des robots d'exploration / Loïca Avanthey (2016)  

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Construction de modèles 3D à partir de données vidéo fisheye / Julien Moreau (2016)  

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Photogrammetric computer vision / Wolfgang Förstner (2016)

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