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Residences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])
[article]
Titre : Residences information extraction from Landsat imagery using the multi-parameter decision tree method Type de document : Article/Communication Auteurs : Yujie Yang, Auteur ; Shijie Wang, Auteur ; Xiaoyong Bai, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1621 - 1633 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] albedo
[Termes IGN] analyse spectrale
[Termes IGN] classification par arbre de décision
[Termes IGN] détection de changement
[Termes IGN] détection du bâti
[Termes IGN] eau
[Termes IGN] image Landsat-OLI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] occupation du sol
[Termes IGN] ombre
[Termes IGN] série temporelle
[Termes IGN] seuillage d'imageRésumé : (auteur) The rapid and accurate grasp of changes in residences is crucial for urban planning and urbanisation. However, the traditional methods for extracting residences exists several problems, which lead to inaccurate extraction results. In this study, the Landsat image is used to establish a new method for extracting the residences quickly and accurately. The specific steps are as follows: (1) We calculate surface albedo to exclude the interference of waters and shadows; (2) Using single-band threshold method, we eliminate the interference of shadows; (3) Normalized Difference Vegetation Index is calculated to exclude the effects of vegetation; (4) Roads are removed by calculating the shape index. Verification shows that the accuracy of this extraction method is 92.81%, which is more accurate than the traditional methods and solves the problems existed in the traditional methods. This novel method is a new reference for other land cover research on the technical aspect. Numéro de notice : A2019-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1494760 Date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1494760 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94106
in Geocarto international > vol 34 n° 14 [30/10/2019] . - pp 1621 - 1633[article]Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data / Emanuele Santi in Remote sensing, Vol 11 n° 20 (October-2 2019)
[article]
Titre : Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data Type de document : Article/Communication Auteurs : Emanuele Santi, Auteur ; Mohammed Dabboor, Auteur ; Simone Pettinato, Auteur ; Simonetta Paloscia, Auteur Année de publication : 2019 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] bande C
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] Manitoba (Canada)
[Termes IGN] polarimétrie
[Termes IGN] polarisation
[Termes IGN] réseau neuronal artificiel
[Termes IGN] série temporelle
[Termes IGN] surface cultivéeRésumé : (auteur) This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (O°) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than O°, with correlation coe?cients up to R ' 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of O° and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized O°. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9. Numéro de notice : A2019-555 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11202451 Date de publication en ligne : 22/10/2019 En ligne : https://doi.org/10.3390/rs11202451 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94210
in Remote sensing > Vol 11 n° 20 (October-2 2019) . - 18 p.[article]Sea ice extent detection in the Bohai Sea using Sentinel-3 OLCI data / Hua Su in Remote sensing, Vol 11 n° 20 (October-2 2019)
[article]
Titre : Sea ice extent detection in the Bohai Sea using Sentinel-3 OLCI data Type de document : Article/Communication Auteurs : Hua Su, Auteur ; Bowen Ji, Auteur ; Yunpeng Wang, Auteur Année de publication : 2019 Article en page(s) : 17 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande infrarouge
[Termes IGN] changement climatique
[Termes IGN] Chine, mer de
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] glace de mer
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-OLCI
[Termes IGN] Normalized Difference Snow Index
[Termes IGN] réflectanceRésumé : (auteur) Sea ice distribution is an important indicator of ice conditions and regional climate change in the Bohai Sea (China). In this study, we monitored the spatiotemporal distribution of the Bohai Sea ice in the winter of 2017–2018 by developing sea ice information indexes using 300 m resolution Sentinel-3 Ocean and Land Color Instrument (OLCI) images. We assessed and validated the index performance using Sentinel-2 MultiSpectral Instrument (MSI) images with higher spatial resolution. The results indicate that the proposed Normalized Difference Sea Ice Information Index (NDSIIIOLCI), which is based on OLCI Bands 20 and 21, can be used to rapidly and effectively detect sea ice but is somewhat affected by the turbidity of the seawater in the southern Bohai Sea. The novel Enhanced Normalized Difference Sea Ice Information Index (ENDSIIIOLCI), which builds on NDSIIIOLCI by also considering OLCI Bands 12 and 16, can monitor sea ice more accurately and effectively than NDSIIIOLCI and suffers less from interference from turbidity. The spatiotemporal evolution of the Bohai Sea ice in the winter of 2017–2018 was successfully monitored by ENDSIIIOLCI. The results show that this sea ice information index based on OLCI data can effectively extract sea ice extent for sediment-laden water and is well suited for monitoring the evolution of Bohai Sea ice in winter. Numéro de notice : A2019-557 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11202436 Date de publication en ligne : 29/10/2019 En ligne : https://doi.org/10.3390/rs11202436 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94214
in Remote sensing > Vol 11 n° 20 (October-2 2019) . - 17 p.[article]Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands / Mohammad El Hajj in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)
[article]
Titre : Comparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands Type de document : Article/Communication Auteurs : Mohammad El Hajj, Auteur ; Nicolas Baghdadi, Auteur ; Mehrez Zribi, Auteur Année de publication : 2019 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] humidité du sol
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Water Index
[Termes IGN] réseau neuronal artificiel
[Termes IGN] surface cultivéeRésumé : (auteur) Surface soil moisture (SSM) estimation is of great importance in several areas, such as hydrology, agriculture and risk assessment. C-band SAR (synthetic aperture radar) data have been widely used to estimate SSM, whereas few studies have been performed using L-band SAR due to the low availability of L-band SAR data. In this context, the objective of the present paper is to compare the SSM estimation potentials of the C- (Sentinel-1) and L-bands (PALSAR) for wheat and grassland plots. The inversion approach developed in this study uses neural networks to invert the SAR signal and estimate the SSM. For each radar frequency, the developed neural networks were trained using the following as an input vector: SAR incidence angle, SAR polarization (VV for the C-band and HH for the L-band), and NDVI from optical images. Artificial Neural networks (ANNs) were developed and validated using synthetic and real databases. The results showed that the L-band provided slightly less accurate SSM estimates than the C-band. Moreover, the results showed that the accuracies of the SSM estimates for both frequencies strongly depended on the soil roughness (Hrms) and SSM values. From the synthetic database at SSM values less than 25 vol.%, the ANNs underestimated the SSM for Hrms values less than 1.5 cm and overestimated the SSM for Hrms values greater than 1.5 cm. In addition, the ANNs underestimated the SSM value regardless of the Hrms value when the SSM value was greater than 25 vol.%. An RMSE analysis of the SSM estimates showed that the highest RMSE values were observed for the L-band regardless of the SSM value, and high RMSE values were observed for the C-band only in very wet soil conditions (SSM>25 vol.%). From the real database at NDVI values less than 0.7, the RMSE (root mean square error) of the SSM estimates was 4.6 vol.% for the C-band and 5.3 vol.% for the L-band. Most importantly, the L-band enabled the estimation of the SSM under a well-developed vegetation cover (NDVI > 0.7) with an RMSE of 6.7 vol.%, whereas the C-band SAR signal became completely attenuated for some crops when the NDVI value was greater than 0.7, and thus the estimation of SSM was impossible using the C-band. Numéro de notice : A2019-473 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.05.021 Date de publication en ligne : 29/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.05.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93634
in International journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 13 p.[article]Landsats 1–5 multispectral scanner system sensors radiometric calibration update / Cibele Teixeira-Pinto in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)
[article]
Titre : Landsats 1–5 multispectral scanner system sensors radiometric calibration update Type de document : Article/Communication Auteurs : Cibele Teixeira-Pinto, Auteur ; Obaidul Haque, Auteur ; Esad Micijevic, Auteur ; Dennis L. Helder, Auteur Année de publication : 2019 Article en page(s) : pp 7378 - 7394 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] Arizona (Etats-Unis)
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] coefficient d'étalonnage
[Termes IGN] étalonnage croisé
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] étalonnage radiométrique
[Termes IGN] image Landsat-MSS
[Termes IGN] Mexique
[Termes IGN] réflectance spectraleNuméro de notice : A2019-537 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2913106 Date de publication en ligne : 17/05/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2913106 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94158
in IEEE Transactions on geoscience and remote sensing > Vol 57 n° 10 (October 2019) . - pp 7378 - 7394[article]A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)PermalinkMeasuring phase scintillation at different frequencies with conventional GNSS receivers operating at 1 Hz / Viet Khoi Nguyen in Journal of geodesy, vol 93 n°10 (October 2019)PermalinkMulti-sensor prediction of Eucalyptus stand volume: A support vector approach / Guilherme Silverio Aquino de Souza in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkPerformance evaluation of real-time global ionospheric maps provided by different IGS analysis centers / Xiaodong Ren in GPS solutions, vol 23 n° 4 (October 2019)PermalinkPerformance of Galileo-only dual-frequency absolute positioning using the fully serviceable Galileo constellation / Tomasz Hadas in GPS solutions, vol 23 n° 4 (October 2019)PermalinkTroposphere delay modeling with horizontal gradients for satellite laser ranging / Mateusz Drożdżewski in Journal of geodesy, vol 93 n°10 (October 2019)PermalinkUnmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments / Leigh Tait in Remote sensing, vol 11 n° 19 (October-1 2019)PermalinkMultitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador / Nguyen-Thanh Son in Geocarto international, vol 34 n° 12 ([15/09/2019])PermalinkAn analytic expression for the phase noise of the goldstein–werner filter / Scott Hensley in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkEvaluating the impact of higher-order ionospheric corrections on multi-GNSS ultra-rapid orbit determination / Xinghan Chen in Journal of geodesy, vol 93 n° 9 (September 2019)Permalink