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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)  

Public [article]  inRemote sensing > Vol 11 n° 20 (October-2 2019) . - 18 p. 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 cultivée Résumé : (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 [article]

An unsupervised classification approach for polarimetric SAR data based on the Chernoff distance for complex Wishart distribution / Mohammed Dabboor in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 2 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 2 (July 2013) . - pp 4200 - 4213 Titre : An unsupervised classification approach for polarimetric SAR data based on the Chernoff distance for complex Wishart distribution Type de document : Article/Communication Auteurs : Mohammed Dabboor, Auteur ; Michael Collins, Auteur ; Vassilia Karathanassi, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4200 - 4213 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image radar moirée [Termes IGN] loi de Wishart [Termes IGN] polarimétrie radar Résumé : (Auteur) A new unsupervised classification approach for polarimetric synthetic aperture radar (POLSAR) data is proposed in this paper. The Wishart-Chernoff distance is calculated and used in an agglomerative hierarchical clustering approach. Initial segmentation of POLSAR data into clusters is obtained based on the total backscattering power (SPAN) combined with the entropy, alpha angle, and anisotropy. The complex Wishart clustering is performed to optimize the initialization. Optimized clusters with minimum Wishart-Chernoff distance are merged hierarchically into an appropriate number of classes. The appropriate number of classes is estimated based on the data log-likelihood algorithm. Classification results show that the use of Wishart-Chernoff distance is superior to that of the Wishart test statistic distance. The effectiveness of the proposed Wishart-Chernoff distance is demonstrated using Advanced Land Observing Satellite POLSAR data. Numéro de notice : A2013-377 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227755 En ligne : https://doi.org/10.1109/TGRS.2012.2227755 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32515 [article]

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