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Leveraging in-scene spectra for vegetation species discrimination with MESMA-MDA / Brian D. Bue in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)
[article]
Titre : Leveraging in-scene spectra for vegetation species discrimination with MESMA-MDA Type de document : Article/Communication Auteurs : Brian D. Bue, Auteur ; David R. Thompson, Auteur ; R. Glenn Sellar, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 33 - 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] analyse discriminante
[Termes IGN] espèce végétale
[Termes IGN] image hyperspectrale
[Termes IGN] réflectance végétale
[Termes IGN] signature spectrale
[Termes IGN] spectromètre imageurRésumé : (auteur) We describe an approach to improve Multiple Endmember Spectral Mixture Analysis (MESMA) results for applications involving discrimination among spectrally-similar species, and commonly occur in multispectral and hyperspectral vegetation remote sensing studies. Such applications are inherently difficult, due to the high degree of similarity between distinct species, coupled with potentially high intra-species variability caused by factors such as growing conditions, canopy structure, ambient illumination, or substrate characteristics. We describe a method to map spectra to a feature space where distinctions between plant species are emphasized using a transformation based on Multiclass Discriminant Analysis. We compute this transformation using groups of pixels that represent individual plant canopies similar to the endmembers in MESMA’s spectral library, and describe a technique to automatically select such spectra from a given image. Compared to conventional MESMA, and also to several alternative MESMA formulations, we observe up to twofold increases in accuracy, along with a factor of ten reduction in computation time using our MESMA approach in several species discrimination applications. We demonstrate the effectiveness of our approach for agricultural species discrimination applications using spectra captured by two different imaging spectrometers. Numéro de notice : A2015-850 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.06.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79220
in ISPRS Journal of photogrammetry and remote sensing > vol 108 (October 2015) . - pp 33 - 48[article]Polygonal clustering analysis using multilevel graph-partition / Wanyi Wang in Transactions in GIS, vol 19 n° 5 (October 2015)
[article]
Titre : Polygonal clustering analysis using multilevel graph-partition Type de document : Article/Communication Auteurs : Wanyi Wang, Auteur ; Shihong Du, Auteur ; Zhou Guo, Auteur ; Liqun Luo, Auteur Année de publication : 2015 Article en page(s) : pp 716 – 736 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] analyse de groupement
[Termes IGN] connexité (graphes)
[Termes IGN] distance
[Termes IGN] données spatiotemporelles
[Termes IGN] figure géométrique
[Termes IGN] groupe
[Termes IGN] partition des données
[Termes IGN] polygone
[Termes IGN] similitudeRésumé : (auteur) Existing methods of spatial data clustering have focused on point data, whose similarity can be easily defined. Due to the complex shapes and alignments of polygons, the similarity between non-overlapping polygons is important to cluster polygons. This study attempts to present an efficient method to discover clustering patterns of polygons by incorporating spatial cognition principles and multilevel graph partition. Based on spatial cognition on spatial similarity of polygons, four new similarity criteria (i.e. the distance, connectivity, size and shape) are developed to measure the similarity between polygons, and used to visually distinguish those polygons belonging to the same clusters from those to different clusters. The clustering method with multilevel graph-partition first coarsens the graph of polygons at multiple levels, using the four defined similarities to find clusters with maximum similarity among polygons in the same clusters, then refines the obtained clusters by keeping minimum similarity between different clusters. The presented method is a general algorithm for discovering clustering patterns of polygons and can satisfy various demands by changing the weights of distance, connectivity, size and shape in spatial similarity. The presented method is tested by clustering residential areas and buildings, and the results demonstrate its usefulness and universality. Numéro de notice : A2015-684 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12124 En ligne : http://dx.doi.org/10.1111/tgis.12124 Format de la ressource électronique : Url artticle Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78325
in Transactions in GIS > vol 19 n° 5 (October 2015) . - pp 716 – 736[article]Two dimensional linear discriminant analyses for hyperspectral data / Maryam Imani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)
[article]
Titre : Two dimensional linear discriminant analyses for hyperspectral data Type de document : Article/Communication Auteurs : Maryam Imani, Auteur ; Hassan Ghassemian, Auteur Année de publication : 2015 Article en page(s) : pp 777 - 786 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] classification pixellaire
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] matriceRésumé : (auteur) Most supervised feature extraction methods like linear discriminant analysis (LDA) suffer from the limited number of available training samples. The singularity problem causes LDA to fail in small sample size (SSS) situations. Two dimensional linear discriminant analysis (2DLDA) for feature extraction of hyperspectral images is proposed in this paper which has good efficiency with small training sample size. In this approach, the feature vector of each pixel of hyperspectral image is transformed into a feature matrix. As a result, the data matrices lie in a low-dimensional space. Then, the between-class and within-class scatter matrices are calculated using the matrix form of training samples. The proposed approach has two main advantages: it deals with the SSS problem in hyperspectral data, and also it can extract each number of features (with no limitation) from the original high dimensional data. The proposed method is tested on four widely used hyperspectral datasets. Experimental results confirm that the proposed 2DLDA feature extraction method provides better classification accuracy, with a reasonable computation time, compared to popular supervised feature extraction methods such as generalized discriminant analysis (GDA) and nonparametric weighted feature extraction (NWFE) particularly compared to the 1DLDA in the SSS situation. The experiments show that two dimensional linear discriminant analysis + support vector machine (2DLDA+SVM) is an appropriate choice for feature extraction and classification of hyperspectral images using limited training samples. Numéro de notice : A2015-988 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.10.777 En ligne : https://doi.org/10.14358/PERS.81.10.777 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80269
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 10 (October 2015) . - pp 777 - 786[article]Impact of the North Atlantic oscillation on Southern Europe water distribution: insights from geodetic data / Pierre Valty in Earth Interactions, vol 19 n° 10 (September 2015)
[article]
Titre : Impact of the North Atlantic oscillation on Southern Europe water distribution: insights from geodetic data Type de document : Article/Communication Auteurs : Pierre Valty , Auteur ; Olivier de Viron, Auteur ; Isabelle Panet , Auteur ; Xavier Collilieux , Auteur Année de publication : 2015 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] analyse en composantes principales
[Termes IGN] Atlantique Nord
[Termes IGN] distribution spatiale
[Termes IGN] Europe du sud
[Termes IGN] masse d'eau
[Termes IGN] Noire, mer
[Termes IGN] oscillationRésumé : (auteur) From space gravity and station position data over southern Europe from 2002 to 2010, this study investigates the interannual mass redistributions using principal component analysis. The dominant mode, which appears both in gravity and positioning, results from the North Atlantic Oscillation (NAO). This analysis allows us to isolate and characterize the NAO impact on the mass distribution, which appears centered over the Black Sea and its two main catchment basins, the Danube and Dnieper. Numéro de notice : A2015--072 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1175/EI-D-14-0028.1 Date de publication en ligne : 16/09/2015 En ligne : http://dx.doi.org/10.1175/EI-D-14-0028.1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83241
in Earth Interactions > vol 19 n° 10 (September 2015) . - pp 1 - 16[article]Documents numériques
en open access
Impact of the North Atlantic oscillation ... - pdf éditeurAdobe Acrobat PDF Regional dynamics of terrestrial vegetation productivity and climate feedbacks for territory of Ukraine / Dmytro Movchan in International journal of geographical information science IJGIS, vol 29 n° 8 (August 2015)
[article]
Titre : Regional dynamics of terrestrial vegetation productivity and climate feedbacks for territory of Ukraine Type de document : Article/Communication Auteurs : Dmytro Movchan, Auteur ; Yuriy V. Kostyuchenko, Auteur Année de publication : 2015 Article en page(s) : pp 1490 - 1505 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique
[Termes IGN] analyse en composantes principales
[Termes IGN] biome
[Termes IGN] changement climatique
[Termes IGN] données météorologiques
[Termes IGN] forêt
[Termes IGN] production agricole végétale
[Termes IGN] production primaire brute
[Termes IGN] régression multiple
[Termes IGN] steppe
[Termes IGN] Ukraine
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (Auteur) Climate change has the potential to affect terrestrial ecosystems and, thereby, the carbon cycle. Various vegetation biomes are likely to respond differently to changes in climatic factors. The purpose of this study was to analyse the trends of the terrestrial vegetation productivity and climate drivers on regional levels and relations between ones. The gross primary productivity from the global satellite-based terrestrial production efficiency model (PEM) MOD17 as the vegetation productivity indicator and meteorological data from the weather station network as climatic indicators were used. The analysis covered a period from 2000 to 2012. Correlation analysis was used to quantify the association between the vegetation productivity and climatic indicators for different growing seasons and landscape-climatic zones of Ukraine. The Mann–Kendall trend test was applied to take into account seasonal features. Multiple linear regression models for corresponding seasons and zones have been simulated using the principal component analysis. The results showed no detectable limiting effect of the climatic drivers on plant productivity for forest areas. The limiting effect of the temperature increasing and precipitation amount decreasing for the steppe zone and eastern forest-steppe subzone of Ukraine for summer was observed. Numéro de notice : A2015-606 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1051985 En ligne : https://doi.org/10.1080/13658816.2015.1051985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78035
in International journal of geographical information science IJGIS > vol 29 n° 8 (August 2015) . - pp 1490 - 1505[article]Spectral–spatial classification of hyperspectral images with a superpixel-based discriminative sparse model / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)PermalinkCharacterizing the heterogeneity of the OpenStreetMap data and community / Ding Ma in ISPRS International journal of geo-information, vol 4 n°2 (June 2015)PermalinkCompilation de données radar et optiques pour la cartographie des classes d'occupation du sol aux environs du système lacustre de Bizerte (Tunisie du Nord) / Ibtissem Amri in Photo interprétation, European journal of applied remote sensing, vol 51 n° 2 (juin 2015)PermalinkMulti-label class assignment in land-use modelling / Hichem Omrani in International journal of geographical information science IJGIS, vol 29 n° 6 (June 2015)PermalinkPoints of interest recommendation from GPS trajectories / Yaqiong Liu in International journal of geographical information science IJGIS, vol 29 n° 6 (June 2015)PermalinkRegionalization of youth and adolescent weight metrics for the continental United States using contiguity-constrained clustering and partitioning / Samuel Adu-Prah in Cartographica, vol 50 n° 2 (Summer 2015)PermalinkComplementarity 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)PermalinkSpectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkCAESAR: an approach based on covariance matrix decomposition to improve multibaseline–multitemporal interferometric SAR processing / Gianfranco Fornaro in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)PermalinkCo-clustering geo-referenced time series: exploring spatio-temporal patterns in Dutch temperature data / Xiaojing Wu in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)PermalinkObject-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)PermalinkAutomatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning / Qian Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkContribution of textural information from TerraSAR-X image for forest mapping / Cécile Cazals (2015)PermalinkData-driven feature learning for high resolution urban land-cover classification / Piotr Andrzej Tokarczyk (2015)PermalinkPermalinkPermalinkOptimisation de la configuration d’un instrument superspectral aéroporté pour la classification : application au milieu urbain / Arnaud Le Bris (2015)PermalinkUse of remotely sensed auxiliary data for improving sample-based forest inventories / Svetlana Saarela (2015)PermalinkSubspace-based technique for speckle noise reduction in SAR images / Norashikin Yahya in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)PermalinkA user study of experimental maps for outdoor activities / Juha Oksanen in Cartographica, vol 49 n° 3 (September 2014)Permalink