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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 descripteurs IGN] analyse en composantes indépendantes [Termes descripteurs IGN] Arabie [Termes descripteurs IGN] données géodésiques [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] modèle de déformation tectonique [Termes descripteurs IGN] Népal [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surcharge hydrologique [Termes descripteurs 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 descripteurs IGN] analyse en composantes indépendantes [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] filtrage numérique d'image [Termes descripteurs 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 descripteurs IGN] analyse canonique [Termes descripteurs IGN] analyse en composantes indépendantes [Termes descripteurs IGN] champ aléatoire de Markov [Termes descripteurs IGN] classificateur [Termes descripteurs IGN] classification [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs 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 descripteurs IGN] analyse en composantes indépendantes [Termes descripteurs IGN] effet atmosphérique [Termes descripteurs IGN] positionnement différentiel [Termes descripteurs IGN] positionnement par GPS [Termes descripteurs IGN] précision du positionnement [Termes descripteurs IGN] propagation troposphérique [Termes descripteurs 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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Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial Lidar point cloud data of forest canopies / Lixia Ma in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 679 - 696 Titre : Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial Lidar point cloud data of forest canopies Type de document : Article/Communication Auteurs : Lixia Ma, Auteur ; Guang Zheng, Auteur ; Jan U.H. Eitel, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 679 - 696 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse en composantes indépendantes [Termes descripteurs IGN] canopée [Termes descripteurs IGN] classification automatique [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] photosynthèse [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] reconnaissance de formes [Termes descripteurs IGN] télémétrie laser sur satellite [Termes descripteurs IGN] zone saillante 3D Résumé : (Auteur) Accurate separation of photosynthetic and nonphotosynthetic components in a forest canopy from 3-D terrestrial laser scanning (TLS) data is a challenging but of key importance to understand the spatial distribution of the radiation regime, photosynthetic processes, and carbon and water exchanges of the forest canopy. The objective of this paper was to improve current methods for separating photosynthetic and nonphotosynthetic components in TLS data of forest canopies by adding two additional filters only based on its geometric information. By comparing the proposed approach with the eigenvalues plus color information-based method, we found that the proposed approach could effectively improve the overall producer's accuracy from 62.12% to 95.45%, and the overall classification producer's accuracy would increase from 84.28% to 97.80% as the forest leaf area index (LAI) decreases from 4.15 to 3.13. In addition, variations in tree species had negligible effects on the final classification accuracy, as shown by the overall producer's accuracy for coniferous (93.09%) and broadleaf (94.96%) trees. To remove quantitatively the effects of the woody materials in a forest canopy for improving TLS-based LAI estimates, we also computed the “woody-to-total area ratio” based on the classified linear class points from an individual tree. Automatic classification of the forest point cloud data set will facilitate the application of TLS on retrieving 3-D forest canopy structural parameters, including LAI and leaf and woody area ratios. Numéro de notice : A2016-114 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2015.2459716 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79992 [article]

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A phase space reconstruction based single channel ICA algorithm and its application in dam deformation analysis / W. Dai in Survey review, vol 47 n° 345 (November 2015)  

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Polarimetric incoherent target decomposition by means of independent component analysis / Nikola Besic in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)  

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Density-based clustering for data containing two types of points / Tao Pei in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)  

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An abundance characteristic-based independent component analysis for hyperspectral unmixing / Nan Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

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Spectral–spatial classification of hyperspectral data via morphological component analysis-based image separation / Zhaohui Xue in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

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Narrow-band interference suppresion for SAR based on independent component analysis / Feng Zhou in IEEE Transactions on geoscience and remote sensing, vol 51 n° 10 (October 2013)  

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Separation of global time-variable gravity signals into maximally independent components / E. Forootan in Journal of geodesy, vol 86 n° 7 (July 2012)  

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Classification of very high spatial resolution imagery based on the fusion of edge and multispectral information / X. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 12 (December 2008)

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Applications of ICA for the enhancement and classification of polarimetric SAR images / H. Wang in International Journal of Remote Sensing IJRS, vol 29 n° 6 (March 2008)

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N-FindR method versus independent component analysis for lithological identification in hyperspectral imagery / C. Gomez in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)

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