Descripteur
Documents disponibles dans cette catégorie (11)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesEtendre la recherche sur niveau(x) vers le bas
Lifting scheme-based sparse density feature extraction for remote sensing target detection / Ling Tian in Remote sensing, vol 13 n° 9 (May-1 2021)
Titre : Lifting scheme-based sparse density feature extraction for remote sensing target detection Type de document : Article/Communication Auteurs : Ling Tian, Auteur ; Yu Cao, Auteur ; Zishan Shi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 1862 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de cible
[Termes IGN] données clairsemées
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtrage numérique d'image
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] transformation en ondelettesRésumé : (auteur) The design of backbones is of great significance for enhancing the location and classification precision in the remote sensing target detection task. Recently, various approaches have been proposed on altering the feature extraction density in the backbones to enlarge the receptive field, make features prominent, and reduce computational complexity, such as dilated convolution and deformable convolution. Among them, one of the most widely used methods is strided convolution, but it loses the information about adjacent feature points which leads to the omission of some useful features and the decrease of detection precision. This paper proposes a novel sparse density feature extraction method based on the relationship between the lifting scheme and convolution, which improves the detection precision while keeping the computational complexity almost the same as the strided convolution. Experimental results on remote sensing target detection indicate that our proposed method improves both detection performance and network efficiency. Numéro de notice : A2021-405 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13091862 Date de publication en ligne : 10/05/2021 En ligne : https://doi.org/10.3390/rs13091862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97720
in Remote sensing > vol 13 n° 9 (May-1 2021) . - n° 1862[article]
Titre : A deep learning framework for matching of SAR and optical imagery Type de document : Article/Communication Auteurs : Lloyd Haydn Hughes, Auteur ; Diego Marcos, Auteur ; Sylvain Lobry, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 166 - 179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] appariement d'images
[Termes IGN] apprentissage profond
[Termes IGN] données clairsemées
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données
[Termes IGN] géoréférencement
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] superposition d'imagesRésumé : (auteur) SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. While for both data sources images are usually provided in a georeferenced manner, the geo-localization of optical images is often inaccurate due to propagation of angular measurement errors. Many methods for the matching of homologous image regions exist for both SAR and optical imagery, however, these methods are unsuitable for SAR-optical image matching due to significant geometric and radiometric differences between the two modalities. In this paper, we present a three-step framework for sparse image matching of SAR and optical imagery, whereby each step is encoded by a deep neural network. We first predict regions in each image which are deemed most suitable for matching. A correspondence heatmap is then generated through a multi-scale, feature-space cross-correlation operator. Finally, outliers are removed by classifying the correspondence surface as a positive or negative match. Our experiments show that the proposed approach provides a substantial improvement over previous methods for SAR-optical image matching and can be used to register even large-scale scenes. This opens up the possibility of using both types of data jointly, for example for the improvement of the geo-localization of optical satellite imagery or multi-sensor stereogrammetry. Numéro de notice : A2020-639 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.012 Date de publication en ligne : 03/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96062
in ISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 166 - 179[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020113 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020112 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt
Titre : Multiview automatic target recognition for infrared imagery using collaborative sparse priors Type de document : Article/Communication Auteurs : Xuelu Li, Auteur ; Vishal Monga, Auteur ; Abhijit Mahalanobis, Auteur Année de publication : 2020 Article en page(s) : pp 6776 - 6790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] ajustement de paramètres
[Termes IGN] apprentissage profond
[Termes IGN] détection de cible
[Termes IGN] données clairsemées
[Termes IGN] estimation bayesienne
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à basse résolution
[Termes IGN] image infrarouge
[Termes IGN] reconnaissance automatiqueRésumé : (auteur) The low resolution of infrared (IR) images makes feature extraction for classification of a challenging work. Learning-based methods, therefore, are preferred to be used on such raw imagery. In this article, in order to avoid difficulties in feature extraction, a novel multitask extension of the widely used sparse-representation-classification (SRC) method is proposed in both single and multiview set-ups. That is, the test sample could be a single IR image or images from different views. In both single-view and multiview scenarios, we try to employ collaborative spike and slab priors. This is because the traditional sparsity-inducing measures such as the l0 -row pseudonorm makes it hard to capture the sparse structure of the coefficient matrix when expanded in terms of a training dictionary, and the priors are proved to be able to capture fairly general sparse structures. Furthermore, a joint prior and sparse coefficient estimation method (JPCEM) is proposed for the first time in this article in order to alleviate the need to handpick prior parameters required before classification. Multiple experiments are conducted on a synthetic Comanche Forward Looking IR (FLIR) Automatic Target Recognition (ATR) database collected by Army Research Lab and a challenging mid-wave IR (MWIR) image ATR database made available by the U.S. Army Night Vision and Electronic Sensors Directorate. The final results substantiate the merits of the proposed JPCEM through comparisons with other state-of-the-art methods, including both the ones based on SRC and the ones constructed using deep learning frameworks. Numéro de notice : A2020-584 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2973969 Date de publication en ligne : 26/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2973969 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95908
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 6776 - 6790[article]
Titre : Efficient edge-aware surface mesh reconstruction for urban scenes Type de document : Article/Communication Auteurs : András Bódis-Szomorú, Auteur ; Hayko Riemenschneider, Auteur ; Luc Van Gool, Auteur Année de publication : 2017 Article en page(s) : pp 3 - 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte de profondeur
[Termes IGN] données clairsemées
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] maillage par triangles
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] reconstruction d'objet
[Termes IGN] scène urbaine
[Termes IGN] segmentation d'image
[Termes IGN] structure-from-motion
[Termes IGN] triangulation de DelaunayRésumé : (auteur) We propose an efficient approach for building compact, edge-preserving, view-centric triangle meshes from either dense or sparse depth data, with a focus on modeling architecture in large-scale urban scenes. Our method constructs a 2D base mesh from a preliminary view partitioning, then lifts the base mesh into 3D in a fast vertex depth optimization. Different view partitioning schemes are proposed for imagery and dense depth maps. They guarantee that mesh edges are aligned with crease edges and discontinuities. In particular, we introduce an effective plane merging procedure with a global error guarantee in order to maximally compact the resulting models. Moreover, different strategies for detecting and handling discontinuities are presented. We demonstrate that our approach provides an excellent trade-off between quality and compactness, and is eligible for fast production of polyhedral building models from large-scale urban height maps, as well as, for direct meshing of sparse street-side Structure-from-Motion (SfM) data. Numéro de notice : a2017-431 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cviu.2016.06.002 En ligne : https://doi.org/10.1016/j.cviu.2016.06.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86332
in Computer Vision and image understanding > vol 157 (April 2017) . - pp 3 - 24[article]
Titre : Multi-objective based spectral unmixing for hyperspectral images Type de document : Article/Communication Auteurs : Xia Xu, Auteur ; Zhenwei Shi, Auteur Année de publication : 2017 Article en page(s) : pp 54 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] données clairsemées
[Termes IGN] image hyperspectrale
[Termes IGN] optimisation (mathématiques)Résumé : (Auteur) Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method. Numéro de notice : A2017-071 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.12.010 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.12.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84306
in ISPRS Journal of photogrammetry and remote sensing > vol 124 (February 2017) . - pp 54 - 69[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017023 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017022 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt PermalinkPermalinkPermalinkPermalinkPermalinkPermalink
