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Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning / G. Zheng in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 777 - 787 Titre : Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning Type de document : Article/Communication Auteurs : G. Zheng, Auteur ; L. Moskal, Auteur ; S.H. Kim, Auteur Année de publication : 2013 Article en page(s) : pp 777 - 787 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt [Termes IGN] image hémisphérique [Termes IGN] Leaf Area Index [Termes IGN] projection azimutale équivalente de lambert [Termes IGN] projection stéréographique [Termes IGN] rastérisation [Termes IGN] semis de points [Termes IGN] télémétrie laser aéroporté [Termes IGN] télémétrie laser terrestre Résumé : (Auteur) Terrestrial laser scanner (TLS)-based leaf area index (LAI) retrieval is an appealing concept, due to the ability to capture structural information of canopies as 3D point cloud data (PCD). TLS-based LAI estimation methods promise a nondestructive tool for spatially explicit calibration of LAI estimated by aerial or satellite remote sensing techniques. These methods also overcome the sky condition restrictions of on-ground optical instruments such as hemispherical photography frequently used for LAI estimation. This paper presents a new method for estimating the effective LAI (LAIe) directly from PCD generated by TLS in heterogeneous forests. We converted the 3-D PCD into 2-D raster images, similar to hemispherical photographs, using two geometrical projection techniques in order to estimate gap fraction and LAIe using a linear least squares method. Our results indicated that the TLS-based algorithm was able to capture the variability in LAIe of forest stands with a range of densities. The TLS-based LAIe estimation method explained 89.1% (rmse = 0.01 ; p Numéro de notice : A2013-080 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2205003 En ligne : https://doi.org/10.1109/TGRS.2012.2205003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32218 [article]

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A graph-based classification method for hyperspectral images / J. Bai in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 803 - 817 Titre : A graph-based classification method for hyperspectral images Type de document : Article/Communication Auteurs : J. Bai, Auteur ; S. Xiang, Auteur ; C. Pan, Auteur Année de publication : 2013 Article en page(s) : pp 803 - 817 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme Graph-Cut [Termes IGN] champ aléatoire de Markov [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image hyperspectrale [Termes IGN] segmentation d'image [Termes IGN] signature spectrale Résumé : (Auteur) The goal of this paper is to apply graph cut (GC) theory to the classification of hyperspectral remote sensing images. The task is formulated as a labeling problem on Markov random field (MRF) constructed on the image grid, and GC algorithm is employed to solve this task. In general, a large number of user interactive strikes are necessary to obtain satisfactory segmentation results. Due to the spatial variability of spectral signatures, however, hyperspectral remote sensing images often contain many tiny regions. Labeling all these tiny regions usually needs expensive human labor. To overcome this difficulty, a pixelwise fuzzy classification based on support vector machine (SVM) is first applied. As a result, only pixels with high probabilities are preserved as labeled ones. This generates a pseudouser strike map. This map is then employed for GC to evaluate the truthful likelihoods of class labels and propagate them to the MRF. To evaluate the robustness of our method, we have tested our method on both large and small training sets. Additionally, comparisons are made between the results of SVM, SVM with stacking neighboring vectors, SVM with morphological preprocessing, extraction and classification of homogeneous objects, and our method. Comparative experimental results demonstrate the validity of our method. Numéro de notice : A2013-081 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2205002 En ligne : https://doi.org/10.1109/TGRS.2012.2205002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32219 [article]

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Classification and reconstruction from random projections for hyperspectral imagery / W. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 833 - 843 Titre : Classification and reconstruction from random projections for hyperspectral imagery Type de document : Article/Communication Auteurs : W. Li, Auteur ; S. Prasad, Auteur ; J. Fowler, Auteur Année de publication : 2013 Article en page(s) : pp 833 - 843 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse en composantes principales [Termes IGN] classification dirigée [Termes IGN] classification non dirigée [Termes IGN] image hyperspectrale [Termes IGN] reconstruction d'image Résumé : (Auteur) There is increasing interest in dimensionality reduction through random projections due in part to the emerging paradigm of compressed sensing. It is anticipated that signal acquisition with random projections will decrease signal-sensing costs significantly; moreover, it has been demonstrated that both supervised and unsupervised statistical learning algorithms work reliably within randomly projected subspaces. Capitalizing on this latter development, several class-dependent strategies are proposed for the reconstruction of hyperspectral imagery from random projections. In this approach, each hyperspectral pixel is first classified into one of several pixel groups using either a conventional supervised classifier or an unsupervised clustering algorithm. After the grouping procedure, a suitable reconstruction method, such as compressive projection principal component analysis, is employed independently within each group. Experimental results confirm that such class-dependent reconstruction, which employs statistics pertinent to each class as opposed to the global statistics estimated over the entire data set, results in more accurate reconstructions of hyperspectral pixels from random projections. Numéro de notice : A2013-082 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2204759 En ligne : https://doi.org/10.1109/TGRS.2012.2204759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32220 [article]

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065-2013021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Through-the-wall human motion indication using sparsity-driven change detection / F. Ahmad in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 881 - 890 Titre : Through-the-wall human motion indication using sparsity-driven change detection Type de document : Article/Communication Auteurs : F. Ahmad, Auteur ; M. Amin, Auteur Année de publication : 2013 Article en page(s) : pp 881 - 890 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] détection à travers-le-mur [Termes IGN] détection de changement [Termes IGN] filtrage du signal [Termes IGN] fouillis d'échos [Termes IGN] image radar [Termes IGN] objet mobile [Termes IGN] positionnement en intérieur [Termes IGN] reconstruction d'image Résumé : (Auteur) We consider sparsity-driven change detection (CD) for human motion indication in through-the-wall radar imaging and urban sensing applications. Stationary targets and clutter are removed via CD, which converts a populated scene into a sparse scene of a few human targets moving inside enclosed structures and behind walls. We establish appropriate CD models for various possible human motions, ranging from translational motions to sudden short movements of the limbs, head, and/or torso. These models permit scene reconstruction within the compressive sensing framework. Results based on laboratory experiments show that a sizable reduction in the data volume is achieved using the proposed approach without a degradation in system performance. Numéro de notice : A2013-083 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2203310 En ligne : https://doi.org/10.1109/TGRS.2012.2203310 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32221 [article]

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Joint wall mitigation and compressive sensing for indoor image reconstruction / E. Lagunas in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 891 - 906 Titre : Joint wall mitigation and compressive sensing for indoor image reconstruction Type de document : Article/Communication Auteurs : E. Lagunas, Auteur ; M. Armin, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 891 - 906 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] acquisition comprimée [Termes IGN] carte d'intérieur [Termes IGN] détection à travers-le-mur [Termes IGN] fouillis d'échos [Termes IGN] image radar [Termes IGN] objet mobile [Termes IGN] positionnement en intérieur [Termes IGN] reconstruction d'image Résumé : (Auteur) Compressive sensing (CS) for urban operations and through-the-wall radar imaging has been shown to be successful in fast data acquisition and moving target localizations. The research in this area thus far has assumed effective removal of wall electromagnetic backscatterings prior to CS application. Wall clutter mitigation can be achieved using full data volume which is, however, in contradiction with the underlying premise of CS. In this paper, we enable joint wall clutter mitigation and CS application using a reduced set of spatial-frequency observations in stepped frequency radar platforms. Specifically, we demonstrate that wall mitigation techniques, such as spatial filtering and subspace projection, can proceed using fewer measurements. We consider both cases of having the same reduced set of frequencies at each of the available antenna locations and also when different frequency measurements are employed at different antenna locations. The latter casts a more challenging problem, as it is not amenable to wall removal using direct implementation of filtering or projection techniques. In this case, we apply CS at each antenna individually to recover the corresponding range profile and estimate the scene response at all frequencies. In applying CS, we use prior knowledge of the wall standoff distance to speed up the convergence of the orthogonal matching pursuit for sparse data reconstruction. Real data are used for validation of the proposed approach. Numéro de notice : A2013-084 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2203824 En ligne : https://doi.org/10.1109/TGRS.2012.2203824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32222 [article]

Exemplaires(1)

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065-2013021	RAB	Revue	Centre de documentation	En réserve L003	Disponible