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Fusion of Landsat 8 OLI and sentinel-2 MSI data / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 7 (July 2017) . - pp 3885 - 3899 Titre : Fusion of Landsat 8 OLI and sentinel-2 MSI data Type de document : Article/Communication Auteurs : Qunming Wang, Auteur ; George Alan Blackburn, Auteur ; Alex O. Onojeghuo, Auteur Année de publication : 2017 Article en page(s) : pp 3885 - 3899 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] pouvoir de résolution géométrique [Termes descripteurs IGN] surveillance Résumé : (Auteur) Sentinel-2 is a wide-swath and fine spatial resolution satellite imaging mission designed for data continuity and enhancement of the Landsat and other missions. The Sentinel-2 data are freely available at the global scale, and have similar wavelengths and the same geographic coordinate system as the Landsat data, which provides an excellent opportunity to fuse these two types of satellite sensor data together. In this paper, a new approach is presented for the fusion of Landsat 8 Operational Land Imager and Sentinel-2 Multispectral Imager data to coordinate their spatial resolutions for continuous global monitoring. The 30 m spatial resolution Landsat 8 bands are downscaled to 10 m using available 10 m Sentinel-2 bands. To account for the land-cover/land-use (LCLU) changes that may have occurred between the Landsat 8 and Sentinel-2 images, the Landsat 8 panchromatic (PAN) band was also incorporated in the fusion process. The experimental results showed that the proposed approach is effective for fusing Landsat 8 with Sentinel-2 data, and the use of the PAN band can decrease the errors introduced by LCLU changes. By fusion of Landsat 8 and Sentinel-2 data, more frequent observations can be produced for continuous monitoring (this is particularly valuable for areas that can be covered easily by clouds, thereby, contaminating some Landsat or Sentinel-2 observations), and the observations are at a consistent fine spatial resolution of 10 m. The products have great potential for timely monitoring of rapid changes. Numéro de notice : A2017-489 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2017.2683444 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86416 [article]

Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area / Mohamed Barakat A. Gibril in Geocarto international, vol 32 n° 7 (July 2017)  

Public [article]  inGeocarto international > vol 32 n° 7 (July 2017) . - pp 735 - 748 Titre : Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area Type de document : Article/Communication Auteurs : Mohamed Barakat A. Gibril, Auteur ; Suzana Bakar, Auteur ; Kouame Yao, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 735 - 748 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image radar [Termes descripteurs IGN] image Radarsat [Termes descripteurs IGN] Malaisie [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] surface cultivée [Termes descripteurs IGN] utilisation du sol [Termes descripteurs IGN] zone intertropicale Résumé : (Auteur) In this study, we investigated the performance of different fusion and classification techniques for land cover mapping in Hilir Perak, Peninsula Malaysia using RADAR and Landsat-8 images in a predominantly agricultural area. The fusion methods used are Brovey Transform, Wavelet Transform, Ehlers and Layer Stacking and their results classified into seven different land cover classes which include (1) pixel-based classifiers (spectral angle mapper (SAM), maximum likelihood (ML), support vector machine (SVM)) and (2) Object-based (rule-based and standard nearest neighbour (NN)) classifiers. The result shows that pixel-based classification achieved maximum accuracy of the optical data classification using SVM in Landsat-8 with 74.96% accuracy compared to SAM and ML. For multisource data classification, the highest overall accuracy recorded for layer stacking (SVM) was 79.78%, Ehlers fusion (SVM) with 45.57%, Brovey fusion (SVM) with 63.70% and Wavelet fusion (SVM) 61.16%. And for object-based classifiers, the overall classification accuracy is 95.35% for rule-based and 76.33% for NN classifier, respectively. Based on the analysis of their performances, object-based and the rule-based classifiers produced the best classification accuracy from the fused images. Numéro de notice : A2017-453 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1170893 date de publication en ligne : 15/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1170893 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86373 [article]

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Pan-sharpening of Landsat-8 images and its application in calculating vegetation greenness and canopy water contents / Khan Rubayet Rahaman in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)  

Public [article]  inISPRS International journal of geo-information > vol 6 n° 6 (June 2017) . - pp Titre : Pan-sharpening of Landsat-8 images and its application in calculating vegetation greenness and canopy water contents Type de document : Article/Communication Auteurs : Khan Rubayet Rahaman, Auteur ; Quazi K. Hassan, Auteur ; M. Razu Ahmed, Auteur Année de publication : 2017 Article en page(s) : pp Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Calgary [Termes descripteurs IGN] Enhanced vegetation index [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Normalized Difference Water Index [Termes descripteurs IGN] pansharpening (fusion d'images) Résumé : (Auteur) Pan-sharpening is the process of fusing higher spatial resolution panchromatic (PAN) with lower spatial resolution multispectral (MS) imagery to create higher spatial resolution MS images. Here, our overall objective was to pan-sharpen Landsat-8 images and calculate vegetation greenness (i.e., normalized difference vegetation index (NDVI)), canopy structure (i.e., enhanced vegetation index (EVI)), and canopy water content (i.e., normalized difference water index (NDWI))-related variables. Our proposed methods consisted of: (i) evaluating the relationships between PAN band (0.503–0.676 µm) with a spatial resolution of 15 m and individual MS bands of Landsat-8 from blue (i.e., acquiring in the range 0.452–0.512 µm), green (i.e., 0.533–0.590 µm), red (i.e., 0.636–0.673 µm), near infrared (NIR: 0.851–0.879 µm), shortwave infrared-I (SWIR-I: 1.566–1.651 µm), and SWIR-II (2.107–2.294 µm) bands with a spatial resolution of 30 m; (ii) determining the suitable individual MS bands to be enhanced into the spatial resolution of the PAN band; and (iii) calculating several vegetation greenness and canopy moisture indices (i.e., NDVI, EVI, NDWI-I, and NDWI-II) at 15 m spatial resolution and subsequent validation using their equivalent-values at a spatial resolution of 30 m. Our analysis revealed that strong linear relationships existed between the PAN and most of the MS individual bands of interest except NIR. For example, r2 values were 0.86–0.89 for blue band; 0.89–0.95 for green band; 0.84–0.96 for red band; 0.71–0.79 for SWIR-I band; and 0.71–0.83 for SWIR-II band. As a result, we performed smoothing filter-based intensity modulation method of pan-sharpening to enhance the spatial resolution of 30 m to 15 m. In calculating the vegetation indices, we used the enhanced MS images and resampled the NIR to 15 m. Finally, we evaluated these indices with their equivalents at 30 m spatial resolution and observed strong relationships (i.e., r2 values in the range 0.98–0.99 for NDVI, 0.95–0.98 for EVI, 0.98–1.00 for NDWI). Numéro de notice : A2017-811 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi6060168 En ligne : https://doi.org/10.3390/ijgi6060168 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89251 [article]

A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration / Milad Mahour in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 126 (April 2017) . - pp 56 – 67 Titre : A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration Type de document : Article/Communication Auteurs : Milad Mahour, Auteur ; Valentyn Tolpekin, Auteur ; Alfred Stein, Auteur ; Ali Sharifi, Auteur Année de publication : 2017 Article en page(s) : pp 56 – 67 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] évapotranspiration [Termes descripteurs IGN] image à moyenne résolution [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] Iran [Termes descripteurs IGN] irrigation [Termes descripteurs IGN] krigeage [Termes descripteurs IGN] mise à l'échelle [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] pouvoir de résolution géométrique [Termes descripteurs IGN] température au sol Résumé : (auteur) This research addressed the effects of downscaling cokriging Land Surface Temperature (LST) on estimation of Actual Evapotranspiration (AET) from remote sensing images. Two procedures were followed. We first applied downscaling cokriging to a coarse resolution LST product of MODIS at 1000 m. With its outcome, daily AET of a medium spatial resolution (250 m) was obtained using the Surface Energy Balance System (SEBS). Second, we downscaled a coarse AET map to medium spatial resolution (250 m). For both procedures, the 250 m resolution MODIS NDVI product was used as a co-variable. Validation was carried out using Landsat 8 images, from which LST was derived from the thermal bands. The two procedures were applied to an agricultural area with a traditional irrigation network in Iran. We obtained an average LST value of 305.8 K as compared to a downscaled LST value of 307.0 K. Reference AET estimated with SEBS using Landsat 8 data was equal to 5.756 mm day−1, as compared with a downscaled AET value of 5.571 mm day−1. The RMSE between reference AET and downscaled AET was equal to 1.26 mm day−1 (r = 0.49) and between reference and downscaled LST to 3.67 K (r = 0.48). The study showed that AET values obtained with the two downscaling procedures were similar to each other, but that AET showed a higher spatial variability if obtained with downscaled LST. We concluded that LST had a large effect on producing AET maps from Remote Sensing (RS) images, and that downscaling cokriging was helpful to provide daily AET maps at medium spatial resolution. Numéro de notice : A2017-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.004 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.02.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84508 [article]

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Effect of training class label noise on classification performances for land cover mapping with satellite image time series / Charlotte Pelletier in Remote sensing, vol 9 n° 2 (February 2017)  

Public [article]  inRemote sensing > vol 9 n° 2 (February 2017) . - pp 1 - 24 Titre : Effect of training class label noise on classification performances for land cover mapping with satellite image time series Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Nicolas Champion , Auteur ; Claire Marais-Sicre, Auteur ; Gérard Dedieu, Auteur Année de publication : 2017 Projets : 1-Pas de projet / Article en page(s) : pp 1 - 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] base de données d'occupation du sol [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 SPOT 4 [Termes descripteurs IGN] série temporelle Résumé : (auteur) Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM) and Random Forests (RF). A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise. Numéro de notice : A2017-896 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : doi.org/10.3390/rs9020173 date de publication en ligne : 18/02/2017 En ligne : https://doi.org/10.3390/rs9020173 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91880 [article]

Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas / Charlotte Pelletier in Remote sensing of environment, vol 187 (15 December 2016)  

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Wave period and coastal bathymetry using wave propagation on optical images / Céline Danilo in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)  

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Estimating forest species abundance through linear unmixing of CHRIS/PROBA imagery / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions / Xiaodong Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

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Estimating forest and woodland aboveground biomass using active and passive remote sensing / Zhuoting Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 4 (April 2016)

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An assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series / Charlotte Pelletier (2016)  

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Automatic detection of clouds and shadows using high resolution satellite image time series / Nicolas Champion (2016)  

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A Bayesian network-based method to alleviate the ill-posed inverse problem: A case study on leaf area index and canopy water content retrieval / Xingwen Quan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)  

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Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments / Mbulisi Sibanda in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)  

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Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)  

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