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Monitoring narrow mangrove stands in Baja California Sur, Mexico using linear spectral unmixing / Jonathan B. Thayn in Marine geodesy, Vol 43 n° 5 (September 2020)  

Public [article]  inMarine geodesy > Vol 43 n° 5 (September 2020) . - pp 493 - 508 Titre : Monitoring narrow mangrove stands in Baja California Sur, Mexico using linear spectral unmixing Type de document : Article/Communication Auteurs : Jonathan B. Thayn, Auteur Année de publication : 2020 Article en page(s) : pp 493 - 508 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] image captée par drone [Termes IGN] image Landsat-8 [Termes IGN] mangrove [Termes IGN] Mexique [Termes IGN] réflectance spectrale Résumé : (auteur) Small stands of mangrove trees are difficult to detect and monitor using satellite remote sensing because the width of the narrow strips of vegetation are typically much smaller than the spatial resolution of the imagery. Every mangrove pixel also contains water and bare soil reflectance. Linear spectral unmixing, which estimates the fractional presence of specific land cover types per pixel, was performed on Landsat 8 imagery to detect mangroves on the eastern shoreline of the Bay of La Paz on the Baja California Peninsula of Mexico. Low-altitude aerial imagery collected from a DJI Mavic Pro drone was used as ground-reference data in the accuracy assessment. Continuous fractional presence of mangroves was detected with 80% accuracy and 85% of mangrove area was found. Future work will use linear spectral unmixing to systematically monitor mangrove extent and health in the region relative to expected growth in tourism development. Numéro de notice : A2020-483 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01490419.2020.1751753 date de publication en ligne : 30/04/2020 En ligne : https://doi.org/10.1080/01490419.2020.1751753 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95636 [article]

10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 / Jean-Baptiste Thomas (2020)  

Public Titre : 10th Colour and Visual Computing Symposium 2020 (CVCS 2020), Gjøvik, Norway, and Virtual, September 16-17, 2020 : Proceedings Type de document : Actes de congrès Auteurs : Jean-Baptiste Thomas, Éditeur scientifique Congrès : Congrès: CVCS 2020 Colour and Visual Computing Symposium 2020 (September 16-17, 2020; Gjøvik, Norvège), Auteur Editeur : Gjøvik [Norvège] : Norwegian University of Science and Technology Année de publication : 2020 Conférence : CVCS 2020, Colour and Visual Computing Symposium 16/09/2020 17/09/2020 Gjøvik et en ligne Norvège Open Access Proceedings Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] analyse visuelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couleur (variable spectrale) [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] imagerie médicale [Termes IGN] luminance lumineuse [Termes IGN] peinture [Termes IGN] photographie Numéro de notice : 25892 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Actes En ligne : http://ceur-ws.org/Vol-2688/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96006

Potential 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])  

Public [article]  inGeocarto international > vol 34 n° 14 [30/10/2019] . - pp 1552 - 1567 Titre : Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis Type de document : Article/Communication Auteurs : Divyesh Varade, Auteur ; Anudeep Sure, Auteur ; Onkar Dikshit, Auteur Année de publication : 2019 Article en page(s) : pp 1552 - 1567 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] classification dirigée [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] Inde [Termes IGN] occupation du sol [Termes IGN] réflectance spectrale [Termes IGN] utilisation du sol Résumé : (auteur) This study proposes the development of a multi-sensor, multi-spectral composite from Landsat-8 and Sentinel-2A imagery referred to as ‘LSC’ for land use land cover (LULC) characterisation and compared with respect to the hyperspectral imagery of the EO1: Hyperion sensor. A three-stage evaluation was implemented based on the similarity observed in the spectral response, supervised classification results and endmember abundance information obtained using linear spectral unmixing. The study was conducted for two areas located around Dhundi and Rohtak in Himachal Pradesh and Haryana, respectively. According to the analysis of the spectral reflectance curves, the spectral response of the LSC is capable of identifying major LULC classes. The kappa accuracy of 0.85 and 0.66 was observed for the classification results from LSC and Hyperion data for Dhundi and Rohtak datasets, respectively. The coefficient of determination was found to be above 0.9 for the LULC classes in both the datasets as compared to Hyperion, indicating a good agreement. Thus, these three-stage results indicated the significant potential of a composite derived from freely available multi-sensor multi-spectral imagery as an alternative to hyperspectral imagery for LULC studies. Numéro de notice : A2019-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1497096 date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1497096 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94101 [article]

Unmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil / Sébastien Giordano in IEEE Transactions on geoscience and remote sensing, vol 56 n° 10 (October 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 10 (October 2018) . - pp 5850 - 5862 Titre : Unmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil Type de document : Article/Communication Auteurs : Sébastien Giordano , Auteur ; Grégoire Mercier, Auteur ; Jean-Paul Rudant , Auteur Année de publication : 2018 Projets : 1-Pas de projet / Article en page(s) : pp 5850 - 5862 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] biomasse aérienne [Termes IGN] décomposition spectrale [Termes IGN] données polarimétriques [Termes IGN] forêt [Termes IGN] image Radarsat [Termes IGN] matrice de covariance [Termes IGN] occupation du sol [Termes IGN] polarimétrie radar [Termes IGN] sol nu [Termes IGN] surface forestière Résumé : (auteur) Extracting information from a polarimetric radar representation usually consists in decomposing it with target decomposition algorithms. This first step can be seen as a geometric analysis of the polarimetric information: the identification of physical radar scattering mechanisms. The problem is that average physical parameters are estimated. As a consequence, these parameters might not describe correctly any of the land cover types that can be mixed together into the radar resolution cell. Therefore, using the polarimetric parameters for land cover classification is challenging. The novelty of the method is to propose a thematic analysis of the polarimetric information preceding the geometric one. The objective is to assess if splitting off polarimetric information on a land cover type basis before applying usual target decomposition algorithms can produce more consistent radar scattering mechanisms when land cover classes are mixed inside the radar resolution cell. A cooperative fusion framework in which very high-resolution optical images are used to unmix physical radar scattering mechanisms is proposed. For bare soil and forests, we point out that a linear unmixing model applied to the covariance matrix is able to split off polarimetric information on a land cover type basis. The assessment of the unmixed radar matrices is carried out with polarimetric radar images from the Radarsat-2 satellite. It was found that despite speckle, the reconstructed radar information after the unmixing process is statistically relevant with the observations. The question whether the unmixed radar images contain relevant thematic information is more challenging, but results tend to validate this property. This method could be used to have a better estimation of vegetation biomass in the context of open forested areas. Numéro de notice : A2018-331 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2827258 date de publication en ligne : 09/07/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2827258 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90475 [article]

Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar / Melissa Fedrigo in ISPRS Journal of photogrammetry and remote sensing, vol 136 (February 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 136 (February 2018) . - pp 106 - 119 Titre : Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar Type de document : Article/Communication Auteurs : Melissa Fedrigo, Auteur ; Glenn J. Newnham, Auteur ; Nicholas C. Coops, Auteur ; Darius S. Culvenor, Auteur ; Douglas K. Bolton, Auteur ; Craig R. Nitschke, Auteur Année de publication : 2018 Article en page(s) : pp 106 - 119 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse en composantes principales [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] Australie [Termes IGN] classification par forêts aléatoires [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Eucalyptus (genre) [Termes IGN] forêt tempérée [Termes IGN] peuplement forestier [Termes IGN] prédiction [Termes IGN] strate forestière Résumé : (Auteur) Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the region that have not previously been identified in the EVC maps. The transition between stand types was better characterised by the random forest modelling approach. In contrast, the linear unmixing model placed greater emphasis on field plots selected as endmembers which may not have captured the variability in stand structure within a single stand type. The random forest model had the highest overall accuracy (84%) and Cohen’s kappa coefficient (0.62). However, the classification accuracy was only marginally better than linear unmixing. The random forest model was applied to a region in the Central Highlands of south-eastern Australia to produce maps of stand type probability, including areas of transition (the ‘ecotone’) between rainforest and eucalypt forest. The resulting map provided a detailed delineation of forest classes, which specifically recognised the coalescing of stand types at the landscape scale. This represents a key step towards mapping the structural and spatial complexity of these ecosystems, which is important for both their management and conservation. Numéro de notice : A2018-074 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.018 date de publication en ligne : 29/12/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89438 [article]

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Adaptive linear spectral mixture analysis / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

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Robust sparse hyperspectral unmixing with ℓ2,1 norm / Yong Ma in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

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Comparative analysis on utilisation of linear spectral unmixing and band ratio methods for processing ASTER data to delineate bauxite over a part of Chotonagpur plateau, Jharkhand, India / Arindam Guha in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)  

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Minimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)  

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An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)  

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