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Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests / Nicola Puletti in European journal of remote sensing, vol 49 n° 1 (2016)
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Titre : Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests Type de document : Article/Communication Auteurs : Nicola Puletti, Auteur ; Nicola Camarretta, Auteur ; Piermaria Corona, Auteur Année de publication : 2016 Article en page(s) : pp 157 - 169 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] feuillu
[Termes IGN] forêt
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] matrice de confusion
[Termes IGN] Pinophyta
[Termes IGN] régression multivariée par spline adaptativeRésumé : (auteur) The objective of the present study is the comparison of the combined use of Earth Observation-1 (EO-1) Hyperion Hyperspectral images with the Random Forest (RF), Support Vector Machines (SVM) and Multivariate Adaptive Regression Splines (MARS) classifiers for discriminating forest cover groups, namely broadleaved and coniferous forests. Statistics derived from classification confusion matrix were used to assess the accuracy of the derived thematic maps. We demonstrated that Hyperion data can be effectively used to obtain rapid and accurate large-scale mapping of main forest types (conifers-broadleaved). We also verified higher capability of Hyperion imagery with respect to Landsat data to such an end. Results demonstrate the ability of the three tested classification methods, with small improvements given by SVM in terms of overall accuracy and kappa statistic. Numéro de notice : A2016-832 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164909 En ligne : http://dx.doi.org/10.5721/EuJRS20164909 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82716
in European journal of remote sensing > vol 49 n° 1 (2016) . - pp 157 - 169[article]Fast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images / Song Tu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
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Titre : Fast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images Type de document : Article/Communication Auteurs : Song Tu, Auteur ; Yi Su, Auteur Année de publication : 2016 Article en page(s) : pp 5729 - 5744 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] détection de changement
[Termes IGN] détection de cible
[Termes IGN] détection de contours
[Termes IGN] granularité d'image
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] imagerie médicale
[Termes IGN] précision des donnéesRésumé : (auteur) The active contour model (ACM) is widely used in target detection of optical and medical images, but multiplicative speckle noise largely interferes with its use in synthetic aperture radar (SAR) images. To overcome this difficulty, a region- and edge-based convex ACM with high efficiency is proposed for target detection in small-scale SAR images. Then, a novel detection algorithm, which combines the advantages of a multiscale saliency detection method and the proposed high-efficiency ACM, is presented to address a large-scale and high-resolution SAR image automatically. Target detection experiments in real and simulated SAR images show that the proposed methods outperform classical ACMs and the popular two-parameter constant false alarm rate detector in terms of efficiency and accuracy. Numéro de notice : A2016-861 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2571309 En ligne : https://doi.org/10.1109/TGRS.2016.2571309 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82892
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 10 (October 2016) . - pp 5729 - 5744[article]Habitat change on Horn Island, Mississippi, 1940-2010, determined from textural features in panchromatic vertical aerial imagery / Guy W. Jeter Jr in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)
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Titre : Habitat change on Horn Island, Mississippi, 1940-2010, determined from textural features in panchromatic vertical aerial imagery Type de document : Article/Communication Auteurs : Guy W. Jeter Jr, Auteur ; Gregory Carter, Auteur Année de publication : 2016 Article en page(s) : pp 985 - 994 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] analyse spatiale
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] habitat (nature)
[Termes IGN] habitat d'espèce
[Termes IGN] image optique
[Termes IGN] image panchromatique
[Termes IGN] marais
[Termes IGN] Mississippi (Etats-Unis)
[Termes IGN] texture d'imageRésumé : (auteur) Habitat-type land cover on Horn Island, Mississippi, northern Gulf of Mexico, was estimated for the years 1940 and 2010 using a combination of panchromatic imagery and 2010 ground survey data. A grey-level co-occurrence matrix was applied to compute reflectance coefficient of variation (CV) and texture indices. The relationships of 2010 CV ranges with known habitat types defined training regions of interest in the 1940 imagery as a substitute for 1940 ground data. Texture indices contrast, correlation, energy and entropy then served as input bands for maximum likelihood classifications which produced 1940 and 2010 habitat maps. Analysis determined that wetter habitats on Horn expanded linearly over the seven-decade period. This is attributed to constraints on sediment supply and the impacts of severe storms which led to decreases in soil depth to the water table. If this trend continues, marsh habitat will cover 31% of Horn Island’s land area by 2050. Numéro de notice : A2016-669 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1094527 Date de publication en ligne : 29/10/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1094527 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81901
in Geocarto international > Vol 31 n° 9 - 10 (October - November 2016) . - pp 985 - 994[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Influence of tree species complexity on discrimination performance of vegetation indices / Azadeh Ghiyamat in European journal of remote sensing, vol 49 n° 1 (2016)
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Titre : Influence of tree species complexity on discrimination performance of vegetation indices Type de document : Article/Communication Auteurs : Azadeh Ghiyamat, Auteur ; Helmi Zulhaidi Mohd Shafri, Auteur ; Abdul Rashid Mohamed Shariff, Auteur Année de publication : 2016 Article en page(s) : pp 15 - 37 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 aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] information complexe
[Termes IGN] Pinus nigra corsicana
[Termes IGN] Pinus sylvestris
[Termes IGN] test de performanceRésumé : (auteur) Performance of different vegetation indices (VIs) in combination with single- and multipleendmember (SEM and MEM) for discriminating Corsican and Scots pines with different ages and Broadleaves tree species is demonstrated by using an airborne hyperspectral data. The analysis is performed in three different complexity levels. The results show by increasing tree species complexity, overall accuracy significantly reduced. An overall accuracy up to 90% is obtained from the first category with the least complexity; however, it is reduced to 55% in the third category with the highest complexity. By employing MEM, performance of normalized difference vegetation index (NDVI) is increased by 10%. Numéro de notice : A2016-834 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164902 En ligne : http://dx.doi.org/10.5721/EuJRS20164902 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82723
in European journal of remote sensing > vol 49 n° 1 (2016) . - pp 15 - 37[article]Object-based morphological profiles for classification of remote sensing imagery / Christian Geiss in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
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Titre : Object-based morphological profiles for classification of remote sensing imagery Type de document : Article/Communication Auteurs : Christian Geiss, Auteur ; Martin Klotz, Auteur ; Andreas Schmitt, Auteur ; Hannes Taubenböck, Auteur Année de publication : 2016 Article en page(s) : pp 5952 - 5963 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification automatique
[Termes IGN] classification orientée objet
[Termes IGN] décomposition d'image
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] morphologie mathématique
[Termes IGN] reconstruction d'imageRésumé : (auteur) Morphological operators (MOs) and their enhancements such as morphological profiles (MPs) are subject to a lively scientific contemplation since they are found to be beneficial for, for example, classification of very high spatial resolution panchromatic, multi-, and hyperspectral imagery. They account for spatial structures with differing magnitudes and, thus, provide a comprehensive multilevel description of an image. In this paper, we introduce the concept of object-based MPs (OMPs) to also encode shape-related, topological, and hierarchical properties of image objects in an exhaustive way. Thereby, we seek to benefit from the so-called object-based image analysis framework by partitioning the original image into objects with a segmentation algorithm on multiple scales. The obtained spatial entities (i.e., objects) are used to aggregate multiple sequences obtained with MOs according to statistical measures of central tendency. This strategy is followed to simultaneously preserve and characterize shape properties of objects and enable both the topological and hierarchical decompositions of an image with respect to the progressive application of MOs. Subsequently, supervised classification models are learned by considering this additionally encoded information. Experimental results are obtained with a random forest classifier with heuristically tuned hyperparameters and a wrapper-based feature selection scheme. We evaluated the results for two test sites of panchromatic WorldView-II imagery, which was acquired over an urban environment. In this setting, the proposed OMPs allow for significant improvements with respect to classification accuracy compared to standard MPs (i.e., obtained by paired sequences of erosion, dilation, opening, closing, opening by top-hat, and closing by top-hat operations). Numéro de notice : A2016-864 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2576978 En ligne : https://doi.org/10.1109/TGRS.2016.2576978 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82899
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 10 (October 2016) . - pp 5952 - 5963[article]Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning / Xiaorui Ma in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)
PermalinkA tensor decomposition-based anomaly detection algorithm for hyperspectral image / Xing Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
PermalinkEstimating forest species abundance through linear unmixing of CHRIS/PROBA imagery / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
PermalinkGeometric calibration of a hyperspectral frame camera / Raquel A. de Oliveira in Photogrammetric record, vol 31 n° 155 (September - November 2016)
PermalinkMapping of land cover in northern California with simulated hyperspectral satellite imagery / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
PermalinkNoise removal from hyperspectral image with joint spectral–spatial distributed sparse representation / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
PermalinkRegression wavelet analysis for lossless coding of remote-sensing data / Naoufal Amrani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
PermalinkRetrieval of leaf area index in different plant species using thermal hyperspectral data / Elnaz Neinavaz in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
PermalinkSemiblind hyperspectral unmixing in the presence of spectral library mismatches / Xiao Fu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
PermalinkThe impact of integrating WorldView-2 sensor and environmental variables in estimating plantation forest species aboveground biomass and carbon stocks in uMgeni Catchment, South Africa / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
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