Centre dedocumentation
scientifique

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 descripteurs IGN] analyse linéaire des mélanges spectraux [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] image EO1-Hyperion [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Inde [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] réflectance spectrale [Termes descripteurs 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 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) . - 13 p. 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) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] analyse linéaire des mélanges spectraux [Termes descripteurs IGN] biomasse aérienne [Termes descripteurs IGN] décomposition spectrale [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] forêt [Termes descripteurs IGN] image Radarsat [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] sol nu [Termes descripteurs 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 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 descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] analyse linéaire des mélanges spectraux [Termes descripteurs IGN] Australie [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Eucalyptus (genre) [Termes descripteurs IGN] forêt tempérée [Termes descripteurs IGN] peuplement forestier [Termes descripteurs IGN] prédiction [Termes descripteurs 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 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.018 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]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018021	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2018023	DEP-EXM	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2018022	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

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

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1240 - 1253 Titre : Adaptive linear spectral mixture analysis Type de document : Article/Communication Auteurs : Chein-I Chang, Auteur Année de publication : 2017 Article en page(s) : pp 1240 - 1253 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse linéaire des mélanges spectraux [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image optique [Termes descripteurs IGN] signature spectrale Résumé : (Auteur) This paper presents a theory of adaptive linear spectral mixture analysis (ALSMA), which can implement LSMA using an adaptive linear mixing model (ALMM) that adjusts and varies with spectral signatures adaptively. In doing so, a recursive LSMA (RLSMA) is developed for ALSMA to allow LSMA to update spectral signature by spectral signature without reprocessing LSMA and also to fuse LSMA results obtained by ALMM using different sets of spectral signatures. To form ALMM, the concept of RLSMA-specified virtual dimensionality is further proposed for ALSMA, which not only can find spectral signatures recursively by RLSMA to adjust ALMM but also can automatically determine the number of spectral signatures via Neyman-Pearson detection theory. Numéro de notice : A2017-151 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2620494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84683 [article]

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

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1227 - 1239 Titre : Robust sparse hyperspectral unmixing with ℓ2,1 norm Type de document : Article/Communication Auteurs : Yong Ma, Auteur ; Chang Li, Auteur ; Xiaoguang Mei, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1227 - 1239 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse linéaire des mélanges spectraux [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] matrice creuse [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] pondération Résumé : (Auteur) Sparse unmixing (SU) of hyperspectral data have recently received particular attention for analyzing remote sensing images, which aims at finding the optimal subset of signatures to best model the mixed pixel in the scene. However, most SU methods are based on the commonly admitted linear mixing model, which ignores the possible nonlinear effects (i.e., nonlinearity), and the nonlinearity is merely treated as outlier. Besides, the traditional SU algorithms often adopt the ℓ2 norm loss function, which makes them sensitive to noises and outliers. In this paper, we propose a robust SU (RSU) method with ℓ2,1 norm loss function, which is robust for noises and outliers. Then, the RSU can be solved by the alternative direction method of multipliers. Finally, the experiments on both synthetic data sets and real hyperspectral images demonstrate that the proposed RSU is efficient for solving the hyperspectral SU problem compared with the state-of-the-art algorithms. Numéro de notice : A2017-150 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2616161 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84681 [article]

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)  

Permalink

Spatial machine learning applied to multivariate and multimodal images / Gianni Franchi (2016)  

Permalink

Two dimensional linear discriminant analyses for hyperspectral data / Maryam Imani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)

Permalink

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)  

Permalink

Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)

Permalink

Linear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification / Keng-Hao Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)  

Permalink

A physics-based unmixing method to estimate subpixel temperatures on mixed pixels / Manuel Cubero-Castan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)  

Permalink

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)  

Permalink

Deblurring and sparse unmixing for hyperspectral images / Xi-Le Zhao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Permalink

Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion / N. Yokoya in IEEE Transactions on geoscience and remote sensing, vol 50 n° 2 (February 2012)  

Permalink