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Auteur Christian Heipke |
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Affiner la recherche Interroger des sources externesDeep learning feature representation for image matching under large viewpoint and viewing direction change / Lin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 190 (August 2022)
Titre : Deep learning feature representation for image matching under large viewpoint and viewing direction change Type de document : Article/Communication Auteurs : Lin Chen, Auteur ; Christian Heipke, Auteur Année de publication : 2022 Article en page(s) : pp 94 -112 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] extraction de traits caractéristiques
[Termes IGN] image aérienne oblique
[Termes IGN] orientation d'image
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau neuronal siamois
[Termes IGN] SIFT (algorithme)Résumé : (auteur) Feature based image matching has been a research focus in photogrammetry and computer vision for decades, as it is the basis for many applications where multi-view geometry is needed. A typical feature based image matching algorithm contains five steps: feature detection, affine shape estimation, orientation assignment, description and descriptor matching. This paper contains innovative work in different steps of feature matching based on convolutional neural networks (CNN). For the affine shape estimation and orientation assignment, the main contribution of this paper is twofold. First, we define a canonical shape and orientation for each feature. As a consequence, instead of the usual Siamese CNN, only single branch CNNs needs to be employed to learn the affine shape and orientation parameters, which turns the related tasks from supervised to self supervised learning problems, removing the need for known matching relationships between features. Second, the affine shape and orientation are solved simultaneously. To the best of our knowledge, this is the first time these two modules are reported to have been successfully trained together. In addition, for the descriptor learning part, a new weak match finder is suggested to better explore the intra-variance of the appearance of matched features. For any input feature patch, a transformed patch that lies far from the input feature patch in descriptor space is defined as a weak match feature. A weak match finder network is proposed to actively find these weak match features; they are subsequently used in the standard descriptor learning framework. The proposed modules are integrated into an inference pipeline to form the proposed feature matching algorithm. The algorithm is evaluated on standard benchmarks and is used to solve for the parameters of image orientation of aerial oblique images. It is shown that deep learning feature based image matching leads to more registered images, more reconstructed 3D points and a more stable block geometry than conventional methods. The code is available at https://github.com/Childhoo/Chen_Matcher.git. Numéro de notice : A2022-502 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.06.003 Date de publication en ligne : 14/06/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.06.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101000
in ISPRS Journal of photogrammetry and remote sensing > vol 190 (August 2022) . - pp 94 -112[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2022081 SL Revue Centre de documentation Revues en salle Disponible 081-2022083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt
Titre : Cooperative image orientation considering dynamic objects Type de document : Article/Communication Auteurs : P. Trusheim, Auteur ; Max Mehltretter, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2022 Article en page(s) : pp 169 - 177 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] compensation par faisceaux
[Termes IGN] orientation d'image
[Termes IGN] point d'appui
[Termes IGN] points homologues
[Termes IGN] réseau neuronal artificiel
[Termes IGN] scène urbaine
[Termes IGN] séquence d'imagesRésumé : (auteur) In the context of image orientation, it is commonly assumed that the environment is completely static. This is why dynamic elements are typically filtered out using robust estimation procedures. Especially in urban areas, however, many such dynamic elements are present in the environment, which leads to a noticeable amount of errors that have to be detected via robust adjustment. This problem is even more evident in the case of cooperative image orientation using dynamic objects as ground control points (GCPs), because such dynamic objects carry the relevant information. One way to deal with this challenge is to detect these dynamic objects prior to the adjustment and to process the related image points separately. To do so, a novel methodology to distinguish dynamic and static image points in stereoscopic image sequences is introduced in this paper, using a neural network for the detection of potentially dynamic objects and additional checks via forward intersection. To investigate the effects of the consideration of dynamic points in the adjustment, an image sequence of an inner-city traffic scenario is used; image orientation, as well as the 3D coordinates of tie points, are calculated via a robust bundle adjustment. It is shown that compared to a solution without considering dynamic points, errors in the tie points are significantly reduced, while the median of the precision of all 3D coordinates of the tie points is improved. Numéro de notice : A2022-441 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-1-2022-169-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-1-2022-169-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100775
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-1-2022 (2022 edition) . - pp 169 - 177[article]
Titre : Deep learning for the detection of early signs for forest damage based on satellite imagery Type de document : Article/Communication Auteurs : Dennis Wittich, Auteur ; Franz Rottensteiner, Auteur ; Mirjana Voelsen, Auteur ; Christian Heipke, Auteur ; Sönke Müller, Auteur Année de publication : 2022 Article en page(s) : pp 307 - 315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dégradation de la flore
[Termes IGN] dommage forestier causé par facteurs naturels
[Termes IGN] fonction de perte
[Termes IGN] image Sentinel-MSI
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] surveillance forestièreRésumé : (auteur) We present an approach for detecting early signs for upcoming forest damages by training a Convolutional Neural Network (CNN) for the pixel-wise prediction of the remaining life-time (RLT) of trees in forests based on Sentinel-2 imagery. We focus on a scenario in which reference data are only available for a related task, namely for a bi-temporal pixel-wise classification of forest degradation. This reference is used to train a CNN for the pixel-wise prediction of forest degradation. In this context, we propose a new sub-sampling-based approach for compensating the effects of a heavy class imbalance in the training data. Using the resulting classification model, we predict semi-labels for images of a Sentinel-2 time series, from which training data for a CNN designed to regress the RLT can be derived after some label cleansing. However, due to data gaps in the time series, e.g. caused by clouds, only intervals can be derived for the target variable to be regressed, and for some training pixels one of the interval limits may even be unknown. Consequently, we propose a new loss function for training a CNN for regressing the RLT that only requires the known interval limits. The method is evaluated on a data set in Germany, covering a time-span of 5 years. We show that the proposed sub-sampling strategy for dealing with strong label imbalance when training the classifier significantly reduces the training time compared to other approaches. We further show that our model predicts the RLT with a maximum error of two months for 80% of the forest pixels that die within one year from the acquisition date of the Sentinel-2 image. Numéro de notice : A2022-432 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-307-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-307-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100738
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 307 - 315[article]
Titre : A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases Type de document : Article/Communication Auteurs : Chun Yang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2021 Article en page(s) : pp 38 - 56 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] Allemagne
[Termes IGN] apprentissage profond
[Termes IGN] approche hiérarchique
[Termes IGN] classification automatique d'objets
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image aérienne
[Termes IGN] jointure
[Termes IGN] objet géographique
[Termes IGN] occupation du sol
[Termes IGN] optimisation (mathématiques)
[Termes IGN] utilisation du solRésumé : (Auteur) Land use as contained in geospatial databases constitutes an essential input for different applications such as urban management, regional planning and environmental monitoring. In this paper, a hierarchical deep learning framework is proposed to verify the land use information. For this purpose, a two-step strategy is applied. First, given high-resolution aerial images, the land cover information is determined. To achieve this, an encoder-decoder based convolutional neural network (CNN) is proposed. Second, the pixel-wise land cover information along with the aerial images serves as input for another CNN to classify land use. Because the object catalogue of geospatial databases is frequently constructed in a hierarchical manner, we propose a new CNN-based method aiming to predict land use in multiple levels hierarchically and simultaneously. A so called Joint Optimization (JO) is proposed where predictions are made by selecting the hierarchical tuple over all levels which has the maximum joint class scores, providing consistent results across the different levels. The conducted experiments show that the CNN relying on JO outperforms previous results, achieving an overall accuracy up to 92.5%. In addition to the individual experiments on two test sites, we investigate whether data showing different characteristics can improve the results of land cover and land use classification, when processed together. To do so, we combine the two datasets and undertake some additional experiments. The results show that adding more data helps both land cover and land use classification, especially the identification of underrepresented categories, despite their different characteristics. Numéro de notice : A2021-370 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.022 Date de publication en ligne : 13/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.022 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97774
in ISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 38 - 56[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021071 SL Revue Centre de documentation Revues en salle Disponible 081-2021073 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021072 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt
Titre : Feature detection and description for image matching: from hand-crafted design to deep learning Type de document : Article/Communication Auteurs : Lin Chen, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2021 Article en page(s) : pp 58 - 74 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement automatique
[Termes IGN] appariement d'images
[Termes IGN] appariement de formes
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image aérienne oblique
[Termes IGN] orientation d'image
[Termes IGN] SIFT (algorithme)Résumé : (Auteur) In feature based image matching, distinctive features in images are detected and represented by feature descriptors. Matching is then carried out by assessing the similarity of the descriptors of potentially conjugate points. In this paper, we first shortly discuss the general framework. Then, we review feature detection as well as the determination of affine shape and orientation of local features, before analyzing feature description in more detail. In the feature description review, the general framework of local feature description is presented first. Then, the review discusses the evolution from hand-crafted feature descriptors, e.g. SIFT (Scale Invariant Feature Transform), to machine learning and deep learning based descriptors. The machine learning models, the training loss and the respective training data of learning-based algorithms are looked at in more detail; subsequently the various advantages and challenges of the different approaches are discussed. Finally, we present and assess some current research directions before concluding the paper. Numéro de notice : A2021-297 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1843376 Date de publication en ligne : 17/11/2020 En ligne : https://doi.org/10.1080/10095020.2020.1843376 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97379
in Geo-spatial Information Science > vol 24 n° 1 (March 2021) . - pp 58 - 74[article]vol IV-1/W1 - May 2017 - ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Christian Heipke
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