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Unsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
[article]
Titre : Unsupervised feature learning for land-use scene recognition Type de document : Article/Communication Auteurs : Jiayuan Fan, Auteur ; Tao Chen, Auteur ; Shijian Lu, Auteur Année de publication : 2017 Article en page(s) : pp 2250 - 2261 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse discriminante
[Termes IGN] codage
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] invariant
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] reconnaissance automatique
[Termes IGN] Singapour
[Termes IGN] utilisation du solRésumé : (Auteur) This paper proposes a novel unsupervised feature learning algorithm for land-use scene recognition on very high resolution remote sensing imagery. The proposed technique utilizes a multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex remote sensing sceneries. Unlike the previous sparse coding and bag-of-visual-words-based techniques that rely on the handcrafted feature descriptors such as scale-invariant feature transform, the proposed technique extracts dense low-level features from the raw data, including the visual (RGB) data and near-infrared (NIR) data, using image patches of varying sizes at different layers. The proposed technique has been evaluated on three data sets, including the 21-category UC Merced landuse RGB data set with a 1-ft spatial resolution, the 9-category ground scene RGB-NIR data set, and the 10-category Singapore land-use RGB-NIR data set with a 0.5-m spatial resolution. The experimental results show that the proposed technique outperforms the state-of-the-art methods. Numéro de notice : A2107-174 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2640186 En ligne : https://doi.org/10.1109/TGRS.2016.2640186 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84723
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2250 - 2261[article]Fusion of graph embedding and sparse representation for feature extraction and classification of hyperspectral imagery / Fulin Luo in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 1 (January 2017)
[article]
Titre : Fusion of graph embedding and sparse representation for feature extraction and classification of hyperspectral imagery Type de document : Article/Communication Auteurs : Fulin Luo, Auteur ; Hong Huang, Auteur ; Jiamin Liu, Auteur ; Zezhong Ma, Auteur Année de publication : 2017 Article en page(s) : pp 37 - 46 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] classification
[Termes IGN] extraction
[Termes IGN] fusion de données multisource
[Termes IGN] graphe
[Termes IGN] image hyperspectraleRésumé : (Auteur) The graph embedding algorithms have been widely applied for feature extraction (FE) of hyperspectral imagery (HSI). These methods need to construct a similarity graph with k-nearest neighbors or ∈-radius ball. However, the neighborhood size is difficult to select in real-world applications. To solve the problem, we propose a new unsupervised FE method, called sparsity preserving analysis (SPA), based on sparse representation and graph embedding. The proposed algorithm utilizes sparse representation to obtain the sparse coefficients of data. Then, it constructs a new graph with the sparse coefficients that reveals the sparse properties of data. Finally, the structure of the graph is preserved in low-dimensional space to obtain a transformation matrix for FE. In addition, a new classification method, termed sparse neighborhood classifier (SNC), is designed using the sparse representation-based methodology. It uses the sparse coefficients of a new sample to obtain the similarity weights in each class. Then, the label information of the new sample is obtained by the weights. The classification accuracies of SPA with SNC reach to 86.9 percent and 80.6 percent on PaviaU and Urban HSI data sets, respectively. The results demonstrate that SPA with SNC can effectively extract low-dimensional features and improve the discriminating power for HSI classification. Numéro de notice : A2017-036 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.1.37 En ligne : https://doi.org/10.14358/PERS.83.1.37 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84090
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 1 (January 2017) . - pp 37 - 46[article]Automatic segment-level tree species recognition using high resolution aerial winter imagery / Anton Kuzmin in European journal of remote sensing, vol 49 n° 1 (2016)
[article]
Titre : Automatic segment-level tree species recognition using high resolution aerial winter imagery Type de document : Article/Communication Auteurs : Anton Kuzmin, Auteur ; Lauri Korhonen, Auteur ; Terhikki Manninen, Auteur ; Matti Maltamo, Auteur Année de publication : 2016 Article en page(s) : pp 239 - 259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse discriminante
[Termes IGN] betula pubescens
[Termes IGN] composition floristique
[Termes IGN] forêt boréale
[Termes IGN] hélicoptère
[Termes IGN] hiver
[Termes IGN] image à ultra haute résolution
[Termes IGN] image aérienne
[Termes IGN] neige
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestrisRésumé : (auteur) Our objective was to automatically recognize the species composition of a boreal forest from high-resolution airborne winter imagery. The forest floor was covered by snow so that the contrast between the crowns and the background was maximized. The images were taken from a helicopter flying at low altitude so that fine details of the canopy structure could be distinguished. Segments created by an object-oriented image processing were used as a basis for a linear discriminant analysis, which aimed at separating the three dominant tree species occurring in the area: Scots pine, Norway spruce, and downy birch. In a cross validation, the classification showed an overall accuracy of 81.9%, and a kappa coefficient of 0.73. Numéro de notice : A2016-831 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164914 En ligne : http://dx.doi.org/10.5721/EuJRS20164914 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82714
in European journal of remote sensing > vol 49 n° 1 (2016) . - pp 239 - 259[article]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]Within-stem maps of wood density and water content for characterization of species: a case study on three hardwood and two softwood species / Fleur Longuetaud in Annals of Forest Science, vol 73 n° 3 (September 2016)
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Titre : Within-stem maps of wood density and water content for characterization of species: a case study on three hardwood and two softwood species Type de document : Article/Communication Auteurs : Fleur Longuetaud, Auteur ; Frédéric Mothe, Auteur ; Meriem Fournier, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 601 - 614 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation
[Termes IGN] Abies alba
[Termes IGN] Acer pseudoplatanus
[Termes IGN] analyse discriminante
[Termes IGN] caractérisation
[Termes IGN] densité du bois
[Termes IGN] espèce végétale
[Termes IGN] Fagus sylvatica
[Termes IGN] Pseudotsuga menziesii
[Termes IGN] Quercus (genre)
[Termes IGN] teneur en eau de la végétation
[Termes IGN] tomographie
[Termes IGN] troncRésumé : (auteur) Key message : Variability and interrelations between wood density, water content, and related properties were analyzed by CT scanning of five species. Relative water content of lumens is proposed as the best complement to basic specific gravity for discrimination of species with respect to their functioning.
Context : X-ray computed tomography (CT) is an efficient tool for analysis of wood properties related to density and water content all along a tree stem. Basic specific gravity, an inherent property of the wood material, is well known and widely used in wood sciences.
Aims : The first aim of this study was to describe a method for mapping a set of wood properties within a tree stem. The second objective was to analyze the relations among these properties and to identify the one that offers the best information in addition to basic specific gravity for discrimination of species.
Methods : Wood discs were collected at various heights along a tree stem. We used a method consisting of comparing the CT images of the discs in the green state and after oven drying. Finally, 10 variables were computed for 115 trees of five temperate species: green, oven-dry, and basic specific gravities; moisture content; relative water content; relative water content of lumens; and fractions of air, water, free water, and cell walls.
Results : Maps of wood properties summarizing the radial and vertical variations were obtained, allowing us to highlight species-specific patterns. The five species were discriminated best when plotted in the plane defined by basic specific gravity and relative water content of lumens.
Conclusion : The proposed method is original and simple enough to process large samples. Because it correlated less with basic specific gravity than with moisture content, relative water content of lumens was selected for species characterization. This is the first study of such wood properties at this fine scale within a tree stem, simultaneously and for a substantial number of trees of five species including both hardwoods and softwoods.Numéro de notice : A2016-710 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-016-0555-4 Date de publication en ligne : 20/05/2016 En ligne : https://doi.org/10.1007/s13595-016-0555-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82088
in Annals of Forest Science > vol 73 n° 3 (September 2016) . - pp 601 - 614[article]Sparse and low-rank graph for discriminant analysis of hyperspectral imagery / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkUse of doppler parameters for ship velocity computation in SAR images / Alfredo Renga in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkComparative 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)PermalinkMatrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)PermalinkDiscrimination of deciduous tree species from time series of unmanned aerial system imagery / Jonathan Lisein in Plos one, vol 10 n° 11 (November 2015)PermalinkLeveraging 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)PermalinkTwo dimensional linear discriminant analyses for hyperspectral data / Maryam Imani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)PermalinkSpectral–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)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)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)PermalinkOptimisation de la configuration d’un instrument superspectral aéroporté pour la classification : application au milieu urbain / Arnaud Le Bris (2015)PermalinkEfficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding / Junwei Han in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkHyperspectral image classification using nearest feature line embedding approach / Yang-Lang Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)PermalinkUsing hyperspectral reflectance data to assess biocontrol damage of giant salvinia / James H. Everitt in Geocarto international, vol 28 n° 5-6 (August - October 2013)PermalinkCommercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa / Kabir Yunus Peerbhay in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)PermalinkSupport vector machine for spatial variation / C. Andris in Transactions in GIS, vol 17 n° 1 (February 2013)PermalinkContribution of texture and red-edge band for vegetated areas detection and identification / Arnaud Le Bris (2013)PermalinkA new technique using infrared satellite measurements to improve the accuracy of the CALIPSO cloud-aerosol discrimination method / A. Naeger in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 2 (January 2013)PermalinkSemisupervised local discriminant analysis for feature extraction in hyperspectral images / W. Liao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)Permalink