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Semi-automatic identification of submarine pipelines with synthetic aperture sonar Images / Victor Hugo Fernandes in Marine geodesy, Vol 43 n° 4 (July 2020)
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Titre : Semi-automatic identification of submarine pipelines with synthetic aperture sonar Images Type de document : Article/Communication Auteurs : Victor Hugo Fernandes, Auteur ; Nilcilene Das Graças Medeiros, Auteur ; Dalto Domingues Rodrigues, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 376 - 395 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] canalisation
[Termes IGN] détection de contours
[Termes IGN] filtrage du bruit
[Termes IGN] scène sous-marine
[Termes IGN] sonarRésumé : (Auteur) Synthetic Aperture Sonar (SAS) is a sensor that was designed for hydrographic survey of the seabed. It detects small targets, enabling high geometric resolution images. However, the images generated by SAS are susceptible to speckle noise, which makes digital processing difficult, since the noises are confused with the targets of interest. The goal of this study was to develop a semi-automatic routine for SAS image processing to verify the structural integrity of submarine pipelines. The method presented incorporates four stages: pre-processing to reduce noise and highlight the targets of interest; extraction of features aiming to recognize features related to the pipelines; post-processing to reduce the fragmentation generated during feature extraction; validation to quantify the results from the reference images to estimate the performance of the proposed methodology. The results showed that more than 80% of the submarine pipelines were mapped in the semi-automatic mode, which considerably reduced the time needed to manually identify a large number of pipelines operating on offshore oilfields. Numéro de notice : A2020-248 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2020.1755916 Date de publication en ligne : 25/04/2020 En ligne : https://doi.org/10.1080/01490419.2020.1755916 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95362
in Marine geodesy > Vol 43 n° 4 (July 2020) . - pp 376 - 395[article]Subpixel-pixel-superpixel-based multiview active learning for hyperspectral images classification / Yu Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
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Titre : Subpixel-pixel-superpixel-based multiview active learning for hyperspectral images classification Type de document : Article/Communication Auteurs : Yu Li, Auteur ; Ting Lu, Auteur ; Shutao Li, Auteur Année de publication : 2020 Article en page(s) : pp 4976 - 4988 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse infrapixellaire
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification pixellaire
[Termes IGN] échantillonnage
[Termes IGN] image hyperspectrale
[Termes IGN] image multiple
[Termes IGN] segmentation sémantique
[Termes IGN] superpixelRésumé : (auteur) Active learning (AL) attempts to actively select the most representative or useful training samples in an iterative manner. The aim is to simultaneously improve the classification performance and reduce the manual labeling effort. In this article, a novel subpixel-pixel-superpixel-based multiview AL (MAL) (SPS-MAL) method is proposed for hyperspectral image (HSI) classification. Here, the multiple views are generated via extracting the subpixel-level, pixel-level, and superpixel-level information. The multiple views can reflect various characteristics of HSI, i.e., spectral mixture, spectral discrimination, and spectral–spatial structure. Therefore, the joint use of diverse and complementary information in multiple views will contribute to a better identification ability of different classes. In addition, a coarse-to-fine MAL algorithm is introduced to effectively select the most representative samples with the most uncertainty. Specifically, a disagreement analysis on multiple views and joint posterior probability estimation is used to query unlabeled samples. Along with the expansion of training samples, view-specific confidence scores are estimated to adaptively integrate the classification results of multiple views, according to their discrimination performance. In this way, the classification accuracy will be further boosted while the number of necessary training samples can be significantly reduced. The experimental classification results on three well-known HSIs demonstrate the effectiveness of the proposed SPS-MAL method. Numéro de notice : A2020-392 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2971081 Date de publication en ligne : 14/02/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2971081 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95388
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 7 (July 2020) . - pp 4976 - 4988[article]Using spectral indices to estimate water content and GPP in sphagnum moss and other peatland vegetation / Kirsten J. Lees in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
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Titre : Using spectral indices to estimate water content and GPP in sphagnum moss and other peatland vegetation Type de document : Article/Communication Auteurs : Kirsten J. Lees, Auteur ; Rebekka R. E. Artz, Auteur ; Myroslava Khomik, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4547 - 4557 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spectrale
[Termes IGN] Bryophyta (mousses)
[Termes IGN] Enhanced vegetation index
[Termes IGN] image hyperspectrale
[Termes IGN] Normalized Difference Water Index
[Termes IGN] production primaire brute
[Termes IGN] puits de carbone
[Termes IGN] réflectance spectrale
[Termes IGN] service écosystémique
[Termes IGN] signature spectrale
[Termes IGN] stockage
[Termes IGN] teneur en eau de la végétation
[Termes IGN] tourbièreRésumé : (auteur) Peatlands provide important ecosystem services including carbon storage and biodiversity conservation. Remote sensing shows potential for monitoring peatlands, but most off-the-shelf data products are developed for unsaturated environments and it is unclear how well they can perform in peatland ecosystems. Sphagnum moss is an important peatland genus with specific characteristics which can affect spectral reflectance, and we hypothesized that the prevalence of Sphagnum in a peatland could affect the spectral signature of the area. This article combines results from both laboratory and field experiments to assess the relationship between spectral indices and the moisture content and gross primary productivity (GPP) of peatland (blanket bog) vegetation species. The aim was to consider how well the selected indices perform under a range of conditions, and whether Sphagnum has a significant impact on the relationships tested. We found that both water indices tested [normalized difference water index (NDWI) and floating water band index (fWBI)] were sensitive to the water content changes in Sphagnum moss in the laboratory, and there was little difference between them. Most of the vegetation indices tested [the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), structure insensitive pigment index (SIPI), and chlorophyll index (CIm)] were found to have a strong relationship with GPP both in the laboratory and in the field. The NDVI and EVI are useful for large-scale estimation of GPP, but are sensitive to the proportion of Sphagnum present. The CIm is less affected by different species proportions and might therefore be the best to use in areas where vegetation species cover is unknown. The photochemical reflectance index (PRI) is shown to be best suited to small-scale studies of single species. Numéro de notice : A2020-378 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2961479 Date de publication en ligne : 27/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2961479 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95371
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 7 (July 2020) . - pp 4547 - 4557[article]An Illumination Insensitive descriptor combining the CSLBP features for street view images in augmented reality: experimental studies / Zejun Xiang in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)
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Titre : An Illumination Insensitive descriptor combining the CSLBP features for street view images in augmented reality: experimental studies Type de document : Article/Communication Auteurs : Zejun Xiang, Auteur ; Ronghua Yang, Auteur ; Chang Deng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 33 p. 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] éclairage
[Termes IGN] intensité lumineuse
[Termes IGN] motif binaire local
[Termes IGN] réalité augmentée
[Termes IGN] scène urbaine
[Termes IGN] SIFT (algorithme)
[Termes IGN] SURF (algorithme)Résumé : (auteur) Numéro de notice : A2020-312 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060362 Date de publication en ligne : 01/06/2020 En ligne : https://doi.org/10.3390/ijgi9060362 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95166
in ISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 33 p.[article]Aqueous alteration mapping in Rishabdev ultramafic complex using imaging spectroscopy / Hrishikesh Kumar in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)
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Titre : Aqueous alteration mapping in Rishabdev ultramafic complex using imaging spectroscopy Type de document : Article/Communication Auteurs : Hrishikesh Kumar, Auteur ; A.S. Rajawat, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande spectrale
[Termes IGN] cartographie géologique
[Termes IGN] image à haute résolution
[Termes IGN] image AVIRIS
[Termes IGN] image infrarouge
[Termes IGN] minéral
[Termes IGN] modèle numérique de surface
[Termes IGN] Rajasthan (Inde ; état)
[Termes IGN] réflectance spectrale
[Termes IGN] roche
[Termes IGN] spectroradiométrieRésumé : (auteur) Hyperspectral remote sensing/imaging spectroscopy has enabled precise identification and mapping of hydrothermal alteration mineral assemblages based on diagnostic absorption features of minerals. In the present study, we use Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) datasets acquired over Rishabdev ultramafic suite to derive surficial mineral map using least square based spectral shape matching in wavelength range of diagnostic absorption features of minerals. Resulting mineral map revealed presence of hydrothermally altered serpentine group of minerals and associated alteration products (talc and dolomite) along with clays and phyllosilicates. Mineral maps are validated using field spectral measurements and published geological map. Involvement of low temperature ( Numéro de notice : A2020-449 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2020.102084 Date de publication en ligne : 14/02/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102084 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95525
in International journal of applied Earth observation and geoinformation > vol 88 (June 2020)[article]Ensemble learning for hyperspectral image classification using tangent collaborative representation / Hongjun Su in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)
PermalinkHyperspectral classification with noisy label detection via superpixel-to-pixel weighting distance / Bing Tu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)
PermalinkImproved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation / Eslam Ali in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)
PermalinkObject-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery / Zhenhui Sun in Geocarto international, Vol 35 n° 8 ([01/06/2020])
PermalinkThe geometric imaging model for high-resolution optical remote sensing satellites considering light aberration and atmospheric refraction errors / Mi Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 6 (June 2020)
PermalinkTrajectory drift–compensated solution of a stereo RGB-D mapping system / Shengjun Tang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 6 (June 2020)
PermalinkA water identification method basing on grayscale Landsat 8 OLI images / Zhitian Deng in Geocarto international, vol 35 n° 7 ([15/05/2020])
PermalinkAn integrated approach to registration and fusion of hyperspectral and multispectral images / Yuan Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
PermalinkAssessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery / Allison Lassiter in Plos one, vol 15 n° 5 (May 2020)
PermalinkAssessment of winter season land surface temperature in the Himalayan regions around the Kullu area in India using Landsat-8 data / Divyesh Varade in Geocarto international, vol 35 n° 6 ([01/05/2020])
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