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Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets / Lamin R. Mansaray in Geocarto international, vol 35 n° 10 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 10 [01/08/2020] . - pp 1088 - 1108 Titre : Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Fumin Wang, Auteur ; Jingfeng Huang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1088 - 1108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] rizière [Termes descripteurs IGN] surface cultivée Résumé : (auteur) SVM and RF are widely used in rice mapping. However, their performance with single and different combinations of satellite datasets is rarely reported. Hence we report their rice mapping accuracies for two seasons using Sentinel-1A, Landsat-8 and Sentinel-2A images. The VH and VV polarizations of Sentinel-1A, and two spectral indices (SIs) of Landsat-8 and Sentine1-2A were used to obtain seven datasets (VH, VV, SI, VHVV, VHSI, VVSI and VHVVSI), and on which SVM and RF were applied and accuracies were assessed. VHSI showed the highest overall accuracy for both algorithms in both years. SVM with VHSI had a slightly higher accuracy (90.8%) than RF with VHSI (89.2%) in 2015 while in 2016 RF with VHSI showed a slightly higher accuracy (95.2%) than SVM with VHSI (93.4%). Although they produced equivalent accuracies within years, RF is more sensitive to additional data, given a 6.0% increase from 2015 to 2016 with VHSI. Numéro de notice : A2020-443 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1568586 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1568586 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95501 [article]

Can ensemble techniques improve coral reef habitat classification accuracy using multispectral data? / Mohammad Shawkat Hossain in Geocarto international, vol 35 n° 11 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 11 [01/08/2020] . - pp 214 - 1232 Titre : Can ensemble techniques improve coral reef habitat classification accuracy using multispectral data? Type de document : Article/Communication Auteurs : Mohammad Shawkat Hossain, Auteur ; Aidy M. Muslim, Auteur ; Muhammad Izuan Nadzri, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 214 - 1232 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] carte bathymétrique [Termes descripteurs IGN] Chine, mer de [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification hypercube [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] distribution de Fisher [Termes descripteurs IGN] fond marin [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] Malaisie [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] récif corallien [Termes descripteurs IGN] réflectance spectrale Résumé : (auteur) Remote sensing has potential in studies of the benthic habitat and extracting the reflectance from the data of multispectral sensors, but traditional image classification techniques cannot provide coral habitat maps with adequate accuracy. This study tested five traditional and three ensemble classification techniques on QuickBird for mapping the benthic composition of coral reefs on the Lang Tengah Island (Malaysia). The common techniques, minimum distance, maximum likelihood, K-nearest neighbour, Fisher and parallelepiped techniques were compared with ensemble classifiers, such as majority voting (MV), simple averaging, and mode combination. The per-class accuracy of the habitat detection improved in the ensemble classifiers; in particular, the MV classifier achieved 95%, 65%, 75% and 95% accuracies for coral, sparse coral, coral rubble and sand, respectively. Ensembles increased the accuracy of the habitat mapping classification by 28%, relative to conventional techniques. Thus, the ensemble techniques can be preferred over the traditional for benthic habitat mapping. Numéro de notice : A2020-459 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1557263 date de publication en ligne : 12/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1557263 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95566 [article]

Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 95 - 117 Titre : Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping Type de document : Article/Communication Auteurs : Alvin B. Baloloy, Auteur ; Ariel C. Blanco, Auteur ; Raymund Rhommel StaAna, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 95 - 117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] Asie du sud-est [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] espèce exotique envahissante [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] orthophotographie [Termes descripteurs IGN] Philippines [Termes descripteurs IGN] Short Waves InfraRed [Termes descripteurs IGN] surveillance du littoral Résumé : (auteur) Numéro de notice : A2020-354 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.001 date de publication en ligne : 11/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95240 [article]

Estimates of spaceborne precipitation radar pulsewidth and beamwidth using sea surface echo data / Kaya Kanemaru in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 8 (August 2020) . - pp 5291 - 5303 Titre : Estimates of spaceborne precipitation radar pulsewidth and beamwidth using sea surface echo data Type de document : Article/Communication Auteurs : Kaya Kanemaru, Auteur ; Toshio Iguchi, Auteur ; Takeshi Masaki, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 5291 - 5303 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bande Ku [Termes descripteurs IGN] climat tropical [Termes descripteurs IGN] écho radar [Termes descripteurs IGN] étalonnage de capteur (imagerie) [Termes descripteurs IGN] image GPM [Termes descripteurs IGN] image TRMM-MI [Termes descripteurs IGN] impulsion [Termes descripteurs IGN] précipitation [Termes descripteurs IGN] surface de la mer [Termes descripteurs IGN] surface équivalente radar Résumé : (auteur) Calibration consistency between Ku-band radars flown on the Tropical Rainfall Measuring Mission’s (TRMM’s) precipitation radar (PR) and the global precipitation measurement (GPM) mission’s dual-frequency PR (DPR) can be attained by the use of the normalized radar cross section (NRCS) or σ0 over the oceans. With the use of the sea surface echo (SSE) data obtained from the spaceborne PRs, this article aims to estimate the radar parameters of pulsewidth and beamwidth and to evaluate the bias in the NRCS estimates caused by the discrete range sampling. Since the SSE shape is closely related to the received pulsewidth and the two-way cross-track beamwidth, those parameters are individually estimated from the SSE shapes. The SSE shapes are also used to evaluate the impact of the discrete range sampling on the NRCS statistics. The pulsewidth and beamwidth estimated from the SSEs compare well with the level-1 values and accurately reflect changes in the configuration of the radars. The NRCS statistics in GPM version 06 show that the calibration consistency between GPM KuPR and TRMM PR is evaluated within the range of −0.39 to +0.03 dB (−0.48 to +0.11 dB) with (without) the peak correction. Numéro de notice : A2020-471 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2963090 date de publication en ligne : 22/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2963090 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95574 [article]

Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 10 [01/08/2020] . - pp 1067 - 1087 Titre : Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method Type de document : Article/Communication Auteurs : Vijendra Singh Bramhe, Auteur ; Sanjay Kumar Ghosh, Auteur ; Pradeep Kumar Garg, Auteur Année de publication : 2020 Article en page(s) : pp 1067 - 1087 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] analyse texturale [Termes descripteurs IGN] bati [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] matrice de co-occurence [Termes descripteurs IGN] niveau de gris (image) [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] texture d'image Résumé : (auteur) Information of built-up area is essential for various applications, such as sustainable development or urban planning. Built-up area extraction using optical data is challenging due to spectral confusion between built-up and other classes (bare land or river sand, etc.). Here an automated approach has been proposed to generate built-up maps using spectral-textural features and feature selection techniques. Eight Grey-Level Co-Occurrence Matrix based texture features are extracted using Landsat-8 Operational Land Imager bands and combined with multispectral data. The most informative features are selected from combined spectral-textural dataset using feature selection techniques. Further, Support Vector Machine (SVM) classifiers are trained on labelled samples using optimal features and results are compared with Back Propagation-Neural Network (BP-NN) and k-Nearest Neighbour (k-NN). The results show that inclusion of textural features and applying feature selection methods increases the highest overall accuracy of Linear-SVM, RBF-SVM, BP-NN, and k-NN by 9.20%, 9.09%, 8.42%, and 7.39%, respectively. Numéro de notice : A2020-425 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1566406 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566406 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95489 [article]

Landuse and land cover identification and disaggregating socio-economic data with convolutional neural network / Jingtao Yao in Geocarto international, vol 35 n° 10 ([01/08/2020])  

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Leveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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On-Orbit Calibration of Terra MODIS VIS Bands Using Polarization-Corrected Desert Observations / Amit Angal in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)  

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Recent changes in two outlet glaciers in the Antarctic Peninsula using multi-temporal Landsat and Sentinel-1 data / Carolina L. Simões in Geocarto international, vol 35 n° 11 ([01/08/2020])  

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Towards a semi-automated mapping of Australia native invasive alien Acacia trees using Sentinel-2 and radiative transfer models in South Africa / Cecilia Masemola in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Classification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

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Complete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China / Kun Tan in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

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Cross-calibration of MODIS reflective solar bands with Sentinel 2A/2B MSI instruments / Amit Angal in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

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Improved crop classification with rotation knowledge using Sentinel-1 and -2 time series / Sébastien Giordano in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)  

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Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery / Kasper Johansen in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

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