Centre de documentation
scientifique

Random forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture / Pashrant K. Srivastava in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 8 (August 2021) . - n° 507 Titre : Random forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture Type de document : Article/Communication Auteurs : Pashrant K. Srivastava, Auteur ; George P. Petropoulos, Auteur ; Rajendra Prasad, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 507 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] algorithme génétique [Termes IGN] Angleterre [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] ensachage [Termes IGN] humidité du sol [Termes IGN] image SMOS [Termes IGN] régression des moindres carrés partiels Résumé : (auteur) Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use. Numéro de notice : A2021-595 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10080507 Date de publication en ligne : 27/07/2021 En ligne : https://doi.org/10.3390/ijgi10080507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98220 [article]

Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data / Alexander Cass in Applied geomatics, vol 11 n° 3 (September 2019)  

Public [article]  inApplied geomatics > vol 11 n° 3 (September 2019) . - pp 277 - 288 Titre : Exploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data Type de document : Article/Communication Auteurs : Alexander Cass, Auteur ; George P. Petropoulos, Auteur ; Konstantinos P. Ferentinos, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 277 - 288 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] carte d'occupation du sol [Termes IGN] cartographie thématique [Termes IGN] classification orientée objet [Termes IGN] image Envisat-ASAR [Termes IGN] image Landsat-TM [Termes IGN] image optique [Termes IGN] image radar [Termes IGN] Pays de Galles [Termes IGN] surface cultivée Résumé : (Auteur) Earth Observation (EO) provides a unique means of obtaining information on land use/cover and of its changes, which is of key importance in many scientific and practical applications. EO data is already widely used, for example, in environmental practices or decision-making related to food availability and security. As such, it is imperative to examine the suitability of different EO datasets, including their synergies, in respect to their ability to create products and tools for such practices and to guide effectively such decisions. This work aims at exploring the added value of the synergistic use of optical and radar data (from the Landsat TM and Advanced Synthetic Aperture Radar (ASAR) sensors respectively). Such information can help towards improving the accuracy of land cover classifications from EO datasets. As a case study, the region of Wales in the UK has been used. Two classifications—one based on optical data alone and another one developed from the synergy of optical and RADAR datasets acquired nearly, concurrently were developed for the studied region. Evaluation of the derived land/use cover maps was performed on the basis of the confusion matrix using validation points derived from a Phase 1 habitat map of Wales. The results showed 15% increase in overall accuracy (84% from 69%) and kappa coefficient (0.81 from 0.65) using the synergistic approach over the scenario where only optical data were used in the classification. In addition, McNemar’s test was used to assess the statistical significance of the obtained results. Results of this test provided further confirmed that the use of optical data synergistically with the radar data provides more accurate land use/cover maps in comparison with the use of optical data alone. Numéro de notice : A2019-461 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00258-7 Date de publication en ligne : 13/04/2019 En ligne : https://doi.org/10.1007/s12518-019-00258-7 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93601 [article]

Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region / George P. Petropoulos in Geocarto international, vol 28 n° 1-2 (February - May 2013)  

Public [article]  inGeocarto international > vol 28 n° 1-2 (February - May 2013) . - pp 114 - 129 Titre : Spectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region Type de document : Article/Communication Auteurs : George P. Petropoulos, Auteur ; Krishna Prasad Vadrevu, Auteur ; Chariton Kalaitzidis, Auteur Année de publication : 2013 Article en page(s) : pp 114 - 129 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification orientée objet [Termes IGN] classification Spectral angle mapper [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] image Quickbird [Termes IGN] littoral méditerranéen [Termes IGN] matrice d'erreur [Termes IGN] occupation du sol Résumé : (Auteur) In this study, we test the potential of two different classification algorithms, namely the spectral angle mapper (SAM) and object-based classifier for mapping the land use/cover characteristics using a Hyperion imagery. We chose a study region that represents a typical Mediterranean setting in terms of landscape structure, composition and heterogeneous land cover classes. Accuracy assessment of the land cover classes was performed based on the error matrix statistics. Validation points were derived from visual interpretation of multispectral high resolution QuickBird-2 satellite imagery. Results from both the classifiers yielded more than 70% classification accuracy. However, the object-based classification clearly outperformed the SAM by 7.91% overall accuracy (OA) and a relatively high kappa coefficient. Similar results were observed in the classification of the individual classes. Our results highlight the potential of hyperspectral remote sensing data as well as object-based classification approach for mapping heterogeneous land use/cover in a typical Mediterranean setting. Numéro de notice : A2013-278 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.668950 Date de publication en ligne : 02/04/2012 En ligne : https://doi.org/10.1080/10106049.2012.668950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32416 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2013011	RAB	Revue	Centre de documentation	En réserve L003	Disponible