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Radar Vegetation Index for assessing cotton crop condition using RISAT-1 data / Dipanwita Haldar in Geocarto international, vol 35 n° 4 ([15/03/2020])
[article]
Titre : Radar Vegetation Index for assessing cotton crop condition using RISAT-1 data Type de document : Article/Communication Auteurs : Dipanwita Haldar, Auteur ; Viral Dave, Auteur ; Arundhati Misra, Auteur ; Bimal Bhattacharya, Auteur Année de publication : 2020 Article en page(s) : pp 364 - 375 Note générale : bibliography Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse
[Termes IGN] cultures
[Termes IGN] Gossypium (genre)
[Termes IGN] image Risat-1
[Termes IGN] Inde
[Termes IGN] indice de végétation
[Termes IGN] modèle de simulation
[Termes IGN] polarisation
[Termes IGN] stress hydrique
[Termes IGN] surveillance de la végétation
[Termes IGN] teneur en eau de la végétationRésumé : (auteur) Periodic crop condition monitoring is of prime importance in cotton belt of western India for water stress management. In this article, vegetation water content (VWC) is assessed using Radar Vegetation Index (RVI) derived from the RISAT-1 data during July to September, vegetative to first picking phase, for utilizing its potential for large area cotton condition assessment. The RVI estimation from dual-polarized data has been demonstrated for regional applications. Prediction models of VWC for cotton crop using RVI and in situ ground measurements depicts significant relationship, with R2 varying from 0.5 to 0.6 and RMSE of 0.3–0.7 kg m−2. High correlation exists between RVI with crop age and crop biomass with R2 varying from 0.55 to 0.7, this proves useful for sowing date prediction. The results showed good validation (R2 = 0.8) for operational applications. The estimated VWC was found with 30–35% error above 4 kg m−2 biomasses as compared to 20–25% in lower ranges. Numéro de notice : A2020-290 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1516249 Date de publication en ligne : 01/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1516249 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95118
in Geocarto international > vol 35 n° 4 [15/03/2020] . - pp 364 - 375[article]An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (March 2020)
[article]
Titre : An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data Type de document : Article/Communication Auteurs : Olga Grigorieva, Auteur ; Olga Brovkina, Auteur ; Alisher Saidov, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Betula (genre)
[Termes IGN] carte forestière
[Termes IGN] classification
[Termes IGN] erreur de classification
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] phénologie
[Termes IGN] Pinus (genre)
[Termes IGN] réflectance spectrale
[Termes IGN] République Tchèque
[Termes IGN] Russie
[Termes IGN] signature spectrale
[Termes IGN] variation saisonnièreRésumé : (auteur) his study proposes an original method for tree species classification by satellite remote sensing. The method uses multitemporal multispectral (Landsat OLI) and hyperspectral (Resurs-P) data acquired from determined vegetation periods. The method is based on an original database of spectral features taking into account seasonal variations of tree species spectra. Changes in the spectral signatures of forest classes are analyzed and new spectral–temporal features are created for the classification. Study sites are located in the Czech Republic and northwest (NW) Russia. The differences in spectral reflectance between tree species are shown as statistically significant in the sub-seasons of spring, first half of summer, and main autumn for both study sites. Most of the errors are related to the classification of deciduous species and misclassification of birch as pine (NW Russia site), pine as mixture of pine and spruce, and pine as mixture of spruce and beech (Czech site). Forest species are mapped with accuracy as high as 80% (NW Russia site) and 81% (Czech site). The classification using multitemporal multispectral data has a kappa coefficient 1.7 times higher than does that of classification using a single multispectral image and 1.3 times greater than that of the classification using single hyperspectral images. Potentially, classification accuracy can be improved by the method when applying multitemporal satellite hyperspectral data, such as in using new, near-future products EnMap and/or HyspIRI with high revisit time. Numéro de notice : A2020-324 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14214/sf.10143 Date de publication en ligne : 02/03/2020 En ligne : https://doi.org/10.14214/sf.10143 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95198
in Silva fennica > vol 54 n° 2 (March 2020)[article]Deep SAR-Net: learning objects from signals / Zhongling Huang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)
[article]
Titre : Deep SAR-Net: learning objects from signals Type de document : Article/Communication Auteurs : Zhongling Huang, Auteur ; Mihai Datcu, Auteur ; Zongxu Pan, Auteur ; Bin Lei, Auteur Année de publication : 2020 Article en page(s) : pp 179 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] image Terra
[Termes IGN] matrice de covariance
[Termes IGN] micro-onde
[Termes IGN] polarisation
[Termes IGN] temps-fréquenceRésumé : (Auteur) This paper introduces a novel Synthetic Aperture Radar (SAR) specific deep learning framework for complex-valued SAR images. The conventional deep convolutional neural networks based methods usually take the amplitude information of single-polarization SAR images as the input to learn hierarchical spatial features automatically, which may have difficulties in discriminating objects with similar texture but discriminative scattering patterns. Our novel deep learning framework, Deep SAR-Net, takes complex-valued SAR images into consideration to learn both spatial texture information and backscattering patterns of objects on the ground. On the one hand, we transfer the detected SAR images pre-trained layers to extract spatial features from intensity images. On the other hand, we dig into the Fourier domain to learn physical properties of the objects by joint time-frequency analysis on complex-valued SAR images. We evaluate the effectiveness of Deep SAR-Net on three complex-valued SAR datasets from Sentinel-1 and TerraSAR-X satellite and demonstrate how it works better than conventional deep CNNs, especially on man-made objects classes. The proposed datasets and the trained Deep SAR-Net model with all codes are provided. Numéro de notice : A2020-065 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.016 Date de publication en ligne : 23/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.016 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94583
in ISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 179 - 193[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study / Yanan Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)
[article]
Titre : The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study Type de document : Article/Communication Auteurs : Yanan Yan, Auteur ; Lei Deng, Auteur ; L. Xian-Lin, Auteur Année de publication : 2020 Article en page(s) : pp 161 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] agrégation spatiale
[Termes IGN] anisotropie
[Termes IGN] bande spectrale
[Termes IGN] classification pixellaire
[Termes IGN] détection d'objet
[Termes IGN] dispersion
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] distribution spatiale
[Termes IGN] extraction de la végétation
[Termes IGN] hétérogénéité spatiale
[Termes IGN] modèle de simulation
[Termes IGN] modèle de transfert radiatif
[Termes IGN] réflectance
[Termes IGN] régression linéaire
[Termes IGN] télédétectionRésumé : (auteur) Spectral decomposition of mixed pixels can provide information about the abundance of end members but fails to indicate the spatial distribution of end members in vegetation remote sensing. This work is a significant attempt to use the bidirectional reflectance distribution function (BRDF) characteristics of mixed pixels in the prediction of spatial-heterogeneity metrics. Data sets from this function with different spatial distributions were constructed by the discrete anisotropic radiative transfer model, and three spatial aggregation and dispersion metrics were calculated: percentage of like adjacencies, spatial division index, and aggregation index. A simple linear regression method was used to construct the prediction model of spatial aggregation and dispersion metrics. The potential of multiangle remote sensing model for identifying spatial patterns well was demonstrated, and its importance was found to differ for different spatial aggregation and dispersion metrics. Specifically, the precision of the model based on multiangle reflectance used for predicting the spatial division index could meet a minimum root mean square of 5.95%. The reflectance features from backward observation on the principal plane play the leading role in recognizing the spatial heterogeneity of mixed pixels. The prediction model is sufficiently robust to distinguish the same vegetation with different growth trends, but also performs well when the ground objects have a smaller reflectance difference in the mixed pixels in a certain band. This study is expected to offer a new thought for spatial-heterogeneity identification of ground objects and thus promote the development of remote sensing technology in assessing spatial distribution. Numéro de notice : A2020-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.161 Date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.161 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94775
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 161 - 167[article]A novel fire index-based burned area change detection approach using Landsat-8 OLI data / Sicong Liu in European journal of remote sensing, vol 53 n° 1 (2020)
[article]
Titre : A novel fire index-based burned area change detection approach using Landsat-8 OLI data Type de document : Article/Communication Auteurs : Sicong Liu, Auteur ; Yongjie Zheng, Auteur ; Michele Dalponte, Auteur ; Xiaohua Tong, Auteur Année de publication : 2020 Article en page(s) : pp 104 - 112 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] brûlis
[Termes IGN] détection de changement
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] incendie de forêt
[Termes IGN] seuillage d'image
[Termes IGN] signature spectraleRésumé : (auteur) Change detection from multi-temporal remote sensing images is an effective way to identify the burned areas after forest fires. However, the complex image scenario and the similar spectral signatures in multispectral bands may lead to many false positive errors, which make it difficult to exact the burned areas accurately. In this paper, a novel-burned area change detection approach is proposed. It is designed based on a new Normalized Burn Ratio-SWIR (NBRSWIR) index and an automatic thresholding algorithm. The effectiveness of the proposed approach is validated on three Landsat-8 data sets presenting various fire disaster events worldwide. Compared to eight index-based detection methods that developed in the literature, the proposed approach has the best performance in terms of class separability (2.49, 1.74 and 2.06) and accuracy (98.93%, 98.57% and 99.51%) in detecting the burned areas. Simultaneously, it can also better suppress the complex irrelevant changes in the background. Numéro de notice : A2020-167 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2020.1738900 Date de publication en ligne : 16/03/2020 En ligne : https://doi.org/10.1080/22797254.2020.1738900 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94836
in European journal of remote sensing > vol 53 n° 1 (2020) . - pp 104 - 112[article]10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 / Jean-Baptiste Thomas (2020)PermalinkCartographie sémantique hybride de scènes urbaines à partir de données image et Lidar / Mohamed Boussaha (2020)PermalinkCombination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan / Emal Wali in Remote sensing, vol 12 n° 1 (January 2020)PermalinkEstimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner / Kai Tan in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkFusion d'approches photométriques et géométriques pour la création de modèles 3D / Jean Mélou (2020)PermalinkPermalinkRestitution de profils verticaux de la distribution de gouttes de pluie à partir de mesures au sol et en altitude / Christophe Samboun (2020)PermalinkPolarization dependence of azimuth cutoff from quad-pol SAR images / Huimin Li in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)PermalinkQuantification of the adjacency effect on measurements in the thermal infrared region / Xiaopo Zheng in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)PermalinkPotential of Landsat-8 and Sentinel-2A composite for land use land cover analysis / Divyesh Varade in Geocarto international, vol 34 n° 14 ([30/10/2019])Permalink