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A simplified ICA-based local similarity stereo matching / Suting Chen in The Visual Computer, vol 37 n° 2 (February 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 2 (February 2021) . - pp 411 - 419 Titre : A simplified ICA-based local similarity stereo matching Type de document : Article/Communication Auteurs : Suting Chen, Auteur ; Jinglin Zhang, Auteur ; Meng Jin, Auteur Année de publication : 2021 Article en page(s) : pp 411 - 419 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse en composantes indépendantes [Termes IGN] appariement d'images [Termes IGN] similitude Résumé : (auteur) Since the existing stereo matching methods may fail in the regions of non-textures, boundaries and tiny details, a simplified independent component correlation algorithm (ICA)-based local similarity stereo matching algorithm is proposed. In order to improve the DispNetC, the proposed algorithm first offers the simplified independent component correlation algorithm (SICA) cost aggregation. Then, the algorithm introduces the matching cost volume pyramid, which simplifies the pre-processing process for the ICA. Also, the SICA loss function is defined. Next, the region-wise loss function combined with the pixel-wise loss function is defined as a local similarity loss function to improve the spatial structure of the disparity map. Finally, the SICA loss function is combined with the local similarity loss function, which is defined to estimate the disparity map and to compensate the edge information of the disparity map. Experimental results on KITTI dataset show that the average absolute error of the proposed algorithm is about 37% lower than that of the DispNetC, and its runtime consuming is about 0.6 s lower than that of GC-Net. Numéro de notice : A2021-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01811-x Date de publication en ligne : 15/02/2020 En ligne : https://doi.org/10.1007/s00371-020-01811-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97286 [article]

Identification and extraction of seasonal geodetic signals due to surface load variations / Stacy Larochelle in Journal of geophysical research : Solid Earth, vol 123 n° 12 (December 2018)  

Public [article]  inJournal of geophysical research : Solid Earth > vol 123 n° 12 (December 2018) . - pp 11031 - 11047 Titre : Identification and extraction of seasonal geodetic signals due to surface load variations Type de document : Article/Communication Auteurs : Stacy Larochelle, Auteur ; Adriano Gualandi, Auteur ; Kristel Chanard , Auteur ; Jean-Philippe Avouac, Auteur Année de publication : 2018 Projets : 3-projet - voir note / Article en page(s) : pp 11031 - 11047 Note générale : bibliographie Funding : King Abdullah City for Science and Technology & NSF. Grant Number: EAR‐1821853 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse en composantes indépendantes [Termes IGN] Arabie [Termes IGN] données géodésiques [Termes IGN] Himalaya [Termes IGN] modèle de déformation tectonique [Termes IGN] Népal [Termes IGN] série temporelle [Termes IGN] surcharge hydrologique [Termes IGN] variation saisonnière Résumé : (auteur) Deformation of the Earth's surface associated with redistributions of continental water mass explains, to first order, the seasonal signals observed in geodetic position time series. Discriminating these seasonal signals from other sources of deformation in geodetic measurements is essential to isolate tectonic signals and to monitor spatio‐temporal variations in continental water storage. We propose a new methodology to identify and extract these seasonal signals. The approach uses a variational Bayesian Independent Component Analysis (vbICA) to extract the seasonal signals and a gravity‐based deformation model to identify which of these signals are caused by surface loading. We test the procedure on two study areas, the Arabian Peninsula and the Nepal Himalaya, and find that the technique successfully extracts the seasonal signals with one or two independent components, depending on whether the load is stationary or moving. The approach is robust to spatial heterogeneities inherent to geodetic measurements and can help extract systematic errors in geodetic products (e.g., draconitic errors). We also discuss how to handle the degree‐1 deformation field present in the geodetic data set but not captured by the gravity‐based model. Numéro de notice : A2018-656 Affiliation des auteurs : Géodésie+Ext (mi2018-2019) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2018JB016607 Date de publication en ligne : 22/11/2018 En ligne : https://doi.org/10.1029/2018JB016607 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93521 [article]

Self-taught feature learning for hyperspectral image classification / Ronald Kemker in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2693 - 2705 Titre : Self-taught feature learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Ronald Kemker, Auteur ; Christopher Kanan, Auteur Année de publication : 2017 Article en page(s) : pp 2693 - 2705 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes indépendantes [Termes IGN] apprentissage automatique [Termes IGN] classification dirigée [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtrage numérique d'image [Termes IGN] image hyperspectrale Résumé : (Auteur) In this paper, we study self-taught learning for hyperspectral image (HSI) classification. Supervised deep learning methods are currently state of the art for many machine learning problems, but these methods require large quantities of labeled data to be effective. Unfortunately, existing labeled HSI benchmarks are too small to directly train a deep supervised network. Alternatively, we used self-taught learning, which is an unsupervised method to learn feature extracting frameworks from unlabeled hyperspectral imagery. These models learn how to extract generalizable features by training on sufficiently large quantities of unlabeled data that are distinct from the target data set. Once trained, these models can extract features from smaller labeled target data sets. We studied two self-taught learning frameworks for HSI classification. The first is a shallow approach that uses independent component analysis and the second is a three-layer stacked convolutional autoencoder. Our models are applied to the Indian Pines, Salinas Valley, and Pavia University data sets, which were captured by two separate sensors at different altitudes. Despite large variation in scene type, our algorithms achieve state-of-the-art results across all the three data sets. Numéro de notice : A2017-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2017.2651639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86390 [article]

Hyperspectral image classification with canonical correlation forests / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 1 (January 2017) . - pp 421 - 431 Titre : Hyperspectral image classification with canonical correlation forests Type de document : Article/Communication Auteurs : Junshi Xia, Auteur ; Naoto Yokoya, Auteur ; Akira Iwasaki, Auteur Année de publication : 2017 Article en page(s) : pp 421 - 431 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse canonique [Termes IGN] analyse en composantes indépendantes [Termes IGN] champ aléatoire de Markov [Termes IGN] classificateur [Termes IGN] classification [Termes IGN] classification par forêts aléatoires [Termes IGN] image hyperspectrale [Termes IGN] Rotation Forest classification Résumé : (Auteur) Multiple classifier systems or ensemble learning is an effective tool for providing accurate classification results of hyperspectral remote sensing images. Two well-known ensemble learning classifiers for hyperspectral data are random forest (RF) and rotation forest (RoF). In this paper, we proposed to use a novel decision tree (DT) ensemble method, namely, canonical correlation forest (CCF). More specifically, several individual canonical correlation trees (CCTs) that are binary DTs, which use canonical correlation components for the hyperplane splitting, are used to construct the CCF. Additionally, we adopt the projection bootstrap technique in CCF, in which the full spectral bands are retained for split selection in the projected space. The techniques aforementioned allow the CCF to improve the accuracy of member classifiers and diversity within the ensemble. Furthermore, the CCF is extended to the spectral-spatial frameworks that incorporate Markov random fields, extended multiattribute profiles (EMAPs), and the ensemble of independent component analysis and rolling guidance filter (E-ICA-RGF). Experimental results on six hyperspectral data sets are used to indicate the comparative effectiveness of the proposed method, in terms of accuracy and computational complexity, compared with RF and RoF, and it turns out that CCF is a promising approach for hyperspectral image classification not only with spectral information but also in the spectral-spatial frameworks. Numéro de notice : A2017-021 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi./org/10.1109/TGRS.2016.2607755 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83953 [article]

Variance components estimation of residual errors in GPS precise positioning / Darko Anđić in Geodetski vestnik, vol 60 n° 3 (September - November 2016)  

Public [article]  inGeodetski vestnik > vol 60 n° 3 (September - November 2016) . - pp 467 - 482 Titre : Variance components estimation of residual errors in GPS precise positioning Type de document : Article/Communication Auteurs : Darko Anđić, Auteur Année de publication : 2016 Article en page(s) : pp 467 - 482 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] analyse en composantes indépendantes [Termes IGN] effet atmosphérique [Termes IGN] positionnement différentiel [Termes IGN] positionnement par GPS [Termes IGN] précision du positionnement [Termes IGN] propagation troposphérique [Termes IGN] résidu Résumé : (Auteur) Variance components analysis of residual errors remaining in the GPS double-difference phase observations is presented in this paper. These errors arise due to unmodeled ionospheric, tropospheric and multipath effects and limit the accuracy of n (northwards), e (eastwards) and u (upwards) coordinate estimates. In addition, there are unavoidable pure random errors. An integral approach to variance components estimation of the residual errors is presented herein. The mathematical basis of the approach lies in the two-way nested classification, where one uses a linear model with random effects. It turned out that, for a baseline of 40km in length at mid-latitude region and during a year of the lowest sunspot activity in the 11-year cycle, daily standard deviations estimates of combined tropospheric and ionospheric effects are with intervals of ~1–11mm, ~1–7mm and ~4–51mm for n, e and u coordinate, respectively. In the same order, for the multipath effects, the intervals are ~4–12mm, ~3–9mm and ~8–30mm, while with pure random error those are ~5–9mm, ~4–7mm and ~9–20mm. It is important to say that the highest values arise in summer and the lowest in winter period. Numéro de notice : A2016--065 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article En ligne : http://www.geodetski-vestnik.com/60/3/gv60-3_andic.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82293 [article]

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