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STC-Det: A slender target detector combining shadow and target information in optical satellite images / Zhaoyang Huang in Remote sensing, vol 13 n° 20 (October-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 20 (October-2 2021) . - n° 4183 Titre : STC-Det: A slender target detector combining shadow and target information in optical satellite images Type de document : Article/Communication Auteurs : Zhaoyang Huang, Auteur ; Feng Wang, Auteur ; Hongjian You, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4183 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement automatique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] détection de cible [Termes IGN] fusion de données [Termes IGN] image satellite [Termes IGN] ombre Résumé : (auteur) Object detection has made great progress. However, due to the unique imaging method of optical satellite remote sensing, the detection of slender targets is still insufficient. Specifically, the perspective of optical satellites is small, and the characteristics of slender targets are severely lost during imaging, resulting in insufficient detection task information; at the same time, the appearance of slender targets in the image is greatly affected by the satellite perspective, which is likely to cause insufficient generalization capabilities of conventional detection models. In response to these two points, we have made some improvements. First, in this paper, we introduce the shadow as auxiliary information to complement the trunk features of the target lost in imaging. Second, to reduce the impact of satellite perspective on imaging, in this paper, we use the characteristic that shadow information is not affected by satellite perspective to design STC-Det. STC-Det treats the shadow and the target as two different types of targets and uses the shadow information to assist the detection, reducing the impact of the satellite perspective on detection. Among them, in order to improve the performance of STC-Det, we propose an automatic matching method (AMM) of shadow and target and a feature fusion method (FFM). Finally, this paper proposes a new method to calculate the heatmaps of detectors, which verifies the effectiveness of the proposed network in a visual way. Experiments show that when the satellite perspective is variable, the precision of STC-Det is increased by 1.7%, and when the satellite perspective is small, the precision of STC-Det is increased by 5.2%. Numéro de notice : A2021-804 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204183 Date de publication en ligne : 19/10/2021 En ligne : https://doi.org/10.3390/rs13204183 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98860 [article]

Feature detection and description for image matching: from hand-crafted design to deep learning / Lin Chen in Geo-spatial Information Science, vol 24 n° 1 (March 2021)  

Public [article]  inGeo-spatial Information Science > vol 24 n° 1 (March 2021) . - pp 58 - 74 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 [article]

A points of interest matching method using a multivariate weighting function with gradient descent optimization / Zhou Yang in Transactions in GIS, Vol 25 n° 1 (February 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 1 (February 2021) . - pp 359 - 381 Titre : A points of interest matching method using a multivariate weighting function with gradient descent optimization Type de document : Article/Communication Auteurs : Zhou Yang, Auteur ; Mingjun Wang, Auteur ; Chen Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 359 - 381 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] algorithme du gradient [Termes IGN] appariement automatique [Termes IGN] appariement de données localisées [Termes IGN] apprentissage automatique [Termes IGN] données localisées des bénévoles [Termes IGN] données multisources [Termes IGN] exploration de données [Termes IGN] intégration de données [Termes IGN] point d'intérêt [Termes IGN] pondération [Termes IGN] qualité des données Résumé : (Auteur) Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning. Numéro de notice : A2021-189 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12690 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1111/tgis.12690 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97158 [article]

Reestimating a minimum acceptable geocoding hit rate for conducting a spatial analysis / Alvaro Briz-Redon in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 7 (July 2020) . - pp 1283 - 1305 Titre : Reestimating a minimum acceptable geocoding hit rate for conducting a spatial analysis Type de document : Article/Communication Auteurs : Alvaro Briz-Redon, Auteur ; Francisco Martinez-Ruiz, Auteur ; Francisco Montes, Auteur Année de publication : 2020 Article en page(s) : pp 1283 - 1305 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] appariement automatique [Termes IGN] criminalité [Termes IGN] géocodage [Termes IGN] géopositionnement [Termes IGN] infraction [Termes IGN] simulation [Termes IGN] taux d'échantillonnage Résumé : (auteur) Geocoding consists in converting a textual description of a location into coordinates. Hence, geocoding a dataset of events has to be carried out before performing a spatial analysis of some data. Automated procedures are necessary to geocode large datasets of events, but they can produce errors. Therefore, it is natural to ask oneself what is the minimum percentage of events that should be geocoded. An 85% of success was established 15 years ago as the first estimate of a minimum acceptable rate, becoming a reference for many spatial analysts. In this paper, the goal is reestimating a minimum acceptable geocoding match rate through the same procedure that was employed for computing the first estimate while accounting for some spatial factors that could possibly influence this estimation: intensity, clustering and aggregation levels. Several statistical techniques and the presence of nonrandomly distributed errors are also explored in this context. The results indicate that variations in intensity, clustering and aggregation levels lead to different minimum acceptable geocoding match rates. Furthermore, specific techniques such as cluster detection seem to be especially sensitive to the existence of non-geocoded data. Therefore, the highly approved 85% geocoding rate may need to be raised. Numéro de notice : A2020-303 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1703994 Date de publication en ligne : 18/12/2019 En ligne : https://doi.org/10.1080/13658816.2019.1703994 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95143 [article]

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Efficiently annotating object images with absolute size information using mobile devices / Martin Hofmann in International journal of computer vision, vol 127 n° 2 (February 2019)  

Public [article]  inInternational journal of computer vision > vol 127 n° 2 (February 2019) . - pp 207 - 224 Titre : Efficiently annotating object images with absolute size information using mobile devices Type de document : Article/Communication Auteurs : Martin Hofmann, Auteur ; Marco Seeland, Auteur ; Patrick Mäder, Auteur Année de publication : 2019 Article en page(s) : pp 207 - 224 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] appareil portable [Termes IGN] appariement automatique [Termes IGN] image numérique [Termes IGN] longueur focale Résumé : (Auteur) The projection of a real world scenery to a planar image sensor inherits the loss of information about the 3D structure as well as the absolute dimensions of the scene. For image analysis and object classification tasks, however, absolute size information can make results more accurate. Today, the creation of size annotated image datasets is effort intensive and typically requires measurement equipment not available to public image contributors. In this paper, we propose an effective annotation method that utilizes the camera within smart mobile devices to capture the missing size information along with the image. The approach builds on the fact that with a camera, calibrated to a specific object distance, lengths can be measured in the object’s plane. We use the camera’s minimum focus distance as calibration distance and propose an adaptive feature matching process for precise computation of the scale change between two images facilitating measurements on larger object distances. Eventually, the measured object is segmented and its size information is annotated for later analysis. A user study showed that humans are able to retrieve the calibration distance with a low variance. The proposed approach facilitates a measurement accuracy comparable to manual measurement with a ruler and outperforms state-of-the-art methods in terms of accuracy and repeatability. Consequently, the proposed method allows in-situ size annotation of objects in images without the need for additional equipment or an artificial reference object in the scene. Numéro de notice : A2018-600 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1093-3 Date de publication en ligne : 24/05/2018 En ligne : https://doi.org/10.1007/s11263-018-1093-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92527 [article]

Appariement automatique de données hétérogènes: textes, traces GPS et ressources géographiques / Amine Medad (2018) 

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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)  

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A method for matching crowd-sourced and authoritative geospatial data / Heshan Du in Transactions in GIS, vol 21 n° 2 (April 2017)  

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Two heads are better than one / Brian Curtiss in GEO: Geoconnexion international, vol 15 n° 8 (September 2016)

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Geometrical consistency voting strategy for outlier detection in image matching / Luping Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)  

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Accurate affine invariant image matching using oriented least square / Amin Sedaghat in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 9 (September 2015)

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Detection of potential updates of authoritative spatial databases by fusion of Volunteered Geographical Information from different sources / Stefan Ivanovic (2015)    

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Association-matrix-based sample consensus approach for automated registration of terrestrial laser scans using linear features / Kaleel Al-Durgham in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 11 (November 2014)  

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A robust image matching method based on optimized BaySAC / Zhizhong Kang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 11 (November 2014)  

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Digital elevation quality derived from medium-format digital cameras / Peter T. Y. Shih in Photogrammetric record, vol 28 n° 144 (December 2013 - February 2014)  

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