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Manifold-based sparse representation for hyperspectral image classification / Yuan Yan Tang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 12 (December 2014) . - pp 7606 - 7618 Titre : Manifold-based sparse representation for hyperspectral image classification Type de document : Article/Communication Auteurs : Yuan Yan Tang, Auteur ; Haoliang Yuan, Auteur ; Luoqing Li, Auteur Année de publication : 2014 Article en page(s) : pp 7606 - 7618 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification spectrale [Termes IGN] image hyperspectrale [Termes IGN] représentation multiple Résumé : (Auteur) A sparsity-based model has led to interesting results in hyperspectral image (HSI) classification. Sparse representation from a test sample is used to identify the class label. However, an ℓ1-based sparse algorithm sometimes yields unstable sparse representation. Inspired by recent progress in manifold learning, two manifold-based sparse representation algorithms are proposed to exploit the local structure of the test samples in corresponding sparse representations for enforcing smoothness across neighboring samples' sparse representations. Using techniques from regularization and local invariance, two manifold-based regularization terms are incorporated into the ℓ1-based objective function. Extensive experiments show that our proposed algorithms obtain excellent classification performance on three classic HSIs. Numéro de notice : A2014-637 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2315209 En ligne : https://doi.org/10.1109/TGRS.2014.2315209 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75053 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Semisupervised manifold alignment of multimodal remote sensing images / Devis Tuia in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 12 (December 2014) . - pp 7708 - 7720 Titre : Semisupervised manifold alignment of multimodal remote sensing images Type de document : Article/Communication Auteurs : Devis Tuia, Auteur ; Michele Volpi, Auteur ; Maxime Triolet, Auteur ; Gustau Camps-Valls, Auteur Année de publication : 2014 Article en page(s) : pp 7708 - 7720 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] alignement semi-dirigé [Termes IGN] données multicapteurs [Termes IGN] données multisources [Termes IGN] données multitemporelles [Termes IGN] graphe [Termes IGN] image à très haute résolution [Termes IGN] télédétection spatiale Résumé : (Auteur) We introduce a method for manifold alignment of different modalities (or domains) of remote sensing images. The problem is recurrent when a set of multitemporal, multisource, multisensor, and multiangular images is available. In these situations, images should ideally be spatially coregistered, corrected, and compensated for differences in the image domains. Such procedures require massive interaction of the user, involve tuning of many parameters and heuristics, and are usually applied separately. Changes of sensors and acquisition conditions translate into shifts, twists, warps, and foldings of the (typically nonlinear) manifolds where images lie. The proposed semisupervised manifold alignment (SS-MA) method aligns the images working directly on their manifolds and is thus not restricted to images of the same resolutions, either spectral or spatial. SS-MA pulls close together samples of the same class while pushing those of different classes apart. At the same time, it preserves the geometry of each manifold along the transformation. The method builds a linear invertible transformation to a latent space where all images are alike and reduces to solving a generalized eigenproblem of moderate size. We study the performance of SS-MA in toy examples and in real multiangular, multitemporal, and multisource image classification problems. The method performs well for strong deformations and leads to accurate classification for all domains. A MATLAB implementation of the proposed method is provided at http://isp. uv.es/code/ssma.htm. Numéro de notice : A2014-638 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2317499 En ligne : https://doi.org/10.1109/TGRS.2014.2317499 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75063 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spectral–spatial hyperspectral image classification via multiscale adaptive sparse representation / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 12 (December 2014) . - pp 7738 - 7749 Titre : Spectral–spatial hyperspectral image classification via multiscale adaptive sparse representation Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; Shutao Li, Auteur ; Xudong Kang, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 7738 - 7749 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse multiéchelle [Termes IGN] classification spectrale [Termes IGN] image hyperspectrale [Termes IGN] représentation parcimonieuse Résumé : (Auteur) Sparse representation has been demonstrated to be a powerful tool in classification of hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining a local region for each test pixel and then jointly representing pixels within each region by a set of common training atoms (samples). However, the selection of the optimal region scale (size) for different HSIs with different types of structures is a nontrivial task. In this paper, considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed. The MASR effectively exploits spatial information at multiple scales via an adaptive sparse strategy. The adaptive sparse strategy not only restricts pixels from different scales to be represented by training atoms from a particular class but also allows the selected atoms for these pixels to be varied, thus providing an improved representation. Experiments on several real HSI data sets demonstrate the qualitative and quantitative superiority of the proposed MASR algorithm when compared to several well-known classifiers. Numéro de notice : A2014-639 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2317499 En ligne : https://doi.org/10.1109/TGRS.2014.2317499 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75076 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data / Luke Wallace in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 12 (December 2014) . - pp 7619 - 7628 Titre : Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data Type de document : Article/Communication Auteurs : Luke Wallace, Auteur ; Arko Lucieer, Auteur ; Christopher S. Watson, Auteur Année de publication : 2014 Article en page(s) : pp 7619 - 7628 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] arbre (flore) [Termes IGN] canopée [Termes IGN] contour [Termes IGN] détection de cible [Termes IGN] données lidar [Termes IGN] drone [Termes IGN] Eucalyptus globulus [Termes IGN] hauteur des arbres [Termes IGN] image à très haute résolution [Termes IGN] implémentation (informatique) [Termes IGN] prise de vue aérienne [Termes IGN] semis de points Résumé : (Auteur) Light detection and Ranging (LiDAR) is becoming an increasingly used tool to support decision-making processes within forest operations. Area-based methods that derive information on the condition of a forest based on the distribution of points within the canopy have been proven to produce reliable and consistent results. Individual tree-based methods, however, are not yet used operationally in the industry. This is due to problems in detecting and delineating individual trees under varying forest conditions resulting in an underestimation of the stem count and biases toward larger trees. The aim of this paper is to use high-resolution LiDAR data captured from a small multirotor unmanned aerial vehicle platform to determine the influence of the detection algorithm and point density on the accuracy of tree detection and delineation. The study was conducted in a four-year-old Eucalyptus globulus stand representing an important stage of growth for forest management decision-making process. Five different tree detection routines were implemented, which delineate trees directly from the point cloud, voxel space, and the canopy height model (CHM). The results suggest that both algorithm and point density are important considerations in the accuracy of the detection and delineation of individual trees. The best performing method that utilized both the CHM and the original point cloud was able to correctly detect 98% of the trees in the study area. Increases in point density (from 5 to 50 points/m2) lead to significant improvements (of up to 8%) in the rate of omission for algorithms that made use of the high density of the data. Numéro de notice : A2014-640 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2315649 En ligne : https://doi.org/10.1109/TGRS.2014.2315649 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75077 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014121	RAB	Revue	Centre de documentation	En réserve L003	Disponible