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Trajectory-based place-recognition for efficient large scale localization / Simon Lynen in International journal of computer vision, vol 124 n° 1 (August 2017)  

Public [article]  inInternational journal of computer vision > vol 124 n° 1 (August 2017) . - pp 49 – 64 Titre : Trajectory-based place-recognition for efficient large scale localization Type de document : Article/Communication Auteurs : Simon Lynen, Auteur ; Michael Bosse, Auteur ; Roland Siegwart, Auteur Année de publication : 2017 Article en page(s) : pp 49 – 64 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] alignement semi-dirigé [Termes IGN] analyse comparative [Termes IGN] appariement automatique [Termes IGN] lieu géométrique [Termes IGN] précision de localisation [Termes IGN] reconnaissance automatique Résumé : (auteur) Place recognition is a core competency for any visual simultaneous localization and mapping system. Identifying previously visited places enables the creation of globally accurate maps, robust relocalization, and multi-user mapping. To match one place to another, most state-of-the-art approaches must decide a priori what constitutes a place, often in terms of how many consecutive views should overlap, or how many consecutive images should be considered together. Unfortunately, such threshold dependencies limit their generality to different types of scenes. In this paper, we present a placeless place recognition algorithm using a novel match-density estimation technique that avoids heuristically discretizing the space. Instead, our approach considers place recognition as a problem of continuous matching between image streams, automatically discovering regions of high match density that represent overlapping trajectory segments. The algorithm uses well-studied statistical tests to identify the relevant matching regions which are subsequently passed to an absolute pose algorithm to recover the geometric alignment. We demonstrate the efficiency and accuracy of our methodology on three outdoor sequences, including a comprehensive evaluation against ground-truth from publicly available datasets that shows our approach outperforms several state-of-the-art algorithms for place recognition. Furthermore we compare our overall algorithm to the currently best performing system for global localization and show how we outperform the approach on challenging indoor and outdoor datasets. Numéro de notice : A2017-399 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-016-0947-9 En ligne : https://doi.org/10.1007/s11263-016-0947-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85941 [article]

Improving sensor fusion : a parametric method for the geometric coalignment of airborne hyperspectral and lidar data / Maximilian Brell in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3460 - 3474 Titre : Improving sensor fusion : a parametric method for the geometric coalignment of airborne hyperspectral and lidar data Type de document : Article/Communication Auteurs : Maximilian Brell, Auteur ; Christian Rogass, Auteur ; Karl Segl, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 3460 - 3474 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] alignement semi-dirigé [Termes IGN] appariement géométrique [Termes IGN] données lidar [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] image multicapteur [Termes IGN] points homologues [Termes IGN] superposition d'images [Termes IGN] télémétrie laser aéroporté Résumé : (Auteur) Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration. Numéro de notice : A2016-855 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2518930 En ligne : https://doi.org/10.1109/TGRS.2016.2518930 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82994 [article]

A manifold alignment approach for hyperspectral image visualization with natural color / Danping Liao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3151 - 3162 Titre : A manifold alignment approach for hyperspectral image visualization with natural color Type de document : Article/Communication Auteurs : Danping Liao, Auteur ; Yuntao Qian, Auteur ; Jun Zhou, Auteur ; Yuan Yan Tang, Auteur Année de publication : 2016 Article en page(s) : pp 3151 - 3162 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] alignement semi-dirigé [Termes IGN] appariement de points [Termes IGN] couleur (variable spectrale) [Termes IGN] image à haute résolution [Termes IGN] image en couleur [Termes IGN] image hyperspectrale Résumé : (Auteur) The trichromatic visualization of hundreds of bands in a hyperspectral image (HSI) has been an active research topic. The visualized image shall convey as much information as possible from the original data and facilitate easy image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a new framework for visualizing an HSI with natural color by the fusion of an HSI and a high-resolution color image via manifold alignment. Manifold alignment projects several data sets to a shared embedding space where the matching points between them are pairwise aligned. The embedding space bridges the gap between the high-dimensional spectral space of the HSI and the RGB space of the color image, making it possible to transfer natural color and spatial information in the color image to the HSI. In this way, a visualized image with natural color distribution and fine spatial details can be generated. Another advantage of the proposed method is its flexible data setting for various scenarios. As our approach only needs to search a limited number of matching pixel pairs that present the same object, the HSI and the color image can be captured from the same or semantically similar sites. Moreover, the learned projection function from the hyperspectral data space to the RGB space can be directly applied to other HSIs acquired by the same sensor to achieve a quick overview. Our method is also able to visualize user-specified bands as natural color images, which is very helpful for users to scan bands of interest. Numéro de notice : A2016-849 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2512659 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2512659 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82930 [article]

Multi-label class assignment in land-use modelling / Hichem Omrani in International journal of geographical information science IJGIS, vol 29 n° 6 (June 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 6 (June 2015) . - pp 1023 - 1041 Titre : Multi-label class assignment in land-use modelling Type de document : Article/Communication Auteurs : Hichem Omrani, Auteur ; Fahed Abdallah, Auteur ; Omar Charif, Auteur Année de publication : 2015 Article en page(s) : pp 1023 - 1041 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] alignement semi-dirigé [Termes IGN] analyse multivariée [Termes IGN] apprentissage automatique [Termes IGN] classification barycentrique [Termes IGN] image aérienne [Termes IGN] Luxembourg [Termes IGN] modélisation [Termes IGN] plus proche voisin, algorithme du [Termes IGN] utilisation du sol Résumé : (Auteur) During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling. Numéro de notice : A2015-599 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1008004 En ligne : https://doi.org/10.1080/13658816.2015.1008004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78013 [article]

Semisupervised manifold alignment of multimodal remote sensing images / Devis Tuia in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 12 (December 2014) . - pp 7708 - 7720 Titre : Semisupervised manifold alignment of multimodal remote sensing images Type de document : Article/Communication Auteurs : Devis Tuia, Auteur ; Michele Volpi, Auteur ; Maxime Triolet, Auteur ; Gustau Camps-Valls, Auteur Année de publication : 2014 Article en page(s) : pp 7708 - 7720 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] alignement semi-dirigé [Termes IGN] données multicapteurs [Termes IGN] données multisources [Termes IGN] données multitemporelles [Termes IGN] graphe [Termes IGN] image à très haute résolution [Termes IGN] télédétection spatiale Résumé : (Auteur) We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images. The problem is recurrent when a set of multitemporal, multisource, multisensor, and multiangular images is available. In these situations, images should ideally be spatially coregistered, corrected, and compensated for differences in the image domains. Such procedures require massive interaction of the user, involve tuning of many parameters and heuristics, and are usually applied separately. Changes of sensors and acquisition conditions translate into shifts, twists, warps, and foldings of the (typically nonlinear) manifolds where images lie. The proposed semisupervised manifold alignment (SS-MA) method aligns the images working directly on their manifolds and is thus not restricted to images of the same resolutions, either spectral or spatial. SS-MA pulls close together samples of the same class while pushing those of different classes apart. At the same time, it preserves the geometry of each manifold along the transformation. The method builds a linear invertible transformation to a latent space where all images are alike and reduces to solving a generalized eigenproblem of moderate size. We study the performance of SS-MA in toy examples and in real multiangular, multitemporal, and multisource image classification problems. The method performs well for strong deformations and leads to accurate classification for all domains. A MATLAB implementation of the proposed method is provided at http://isp. uv.es/code/ssma.htm. Numéro de notice : A2014-638 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2317499 En ligne : https://doi.org/10.1109/TGRS.2014.2317499 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75063 [article]

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