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An assessment of the repeatability of automatic forest inventory metrics derived from UAV-borne laser scanning data / Luke Wallace in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 7160 - 7169 Titre : An assessment of the repeatability of automatic forest inventory metrics derived from UAV-borne laser scanning data Type de document : Article/Communication Auteurs : Luke Wallace, Auteur ; Robert Musk, Auteur ; Arko Lucieer, Auteur Année de publication : 2014 Article en page(s) : pp 7160 - 7169 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection de cible [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] semis de points Résumé : (Auteur) We assessed the reproducibility of forest inventory metrics derived from an unmanned aerial vehicle (UAV) laser scanning (UAVLS) system. A total of 82 merged point clouds were captured over six 500-m2 plots within a Eucalyptus globulus plantation forest in Tasmania, Australia. Terrain and understory height, together with plot- and tree-level metrics, were extracted from the UAVLS point clouds using automated methods and compared across the multiple point clouds. The results show that measurements of terrain and understory height and plot-level metrics can be reproduced with adequate repeatability for change detection purposes. At the tree level, the high-density data collected by the UAV provided estimates of tree location (mean deviation (MD) of less than 0.48 m) and tree height (MD of 0.35 m) with high precision. This precision is comparable to that of ground-based field measurement techniques. The estimates of crown area and crown volume were found to be dependent on the segmentation routine and, as such, were measured with lower repeatability. The precision of the metrics found within this paper demonstrates the applicability of UAVs as a platform for performing sample-based forest inventories. Numéro de notice : A2014-539 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2308208 En ligne : https://doi.org/10.1109/TGRS.2014.2308208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74156 [article]

Exemplaires(1)

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065-2014111A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Hyperspectral unmixing with [lq] regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 6793 - 6806 Titre : Hyperspectral unmixing with [lq] regularization Type de document : Article/Communication Auteurs : Jakob Sigurdsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2014 Article en page(s) : pp 6793 - 6806 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] image hyperspectrale [Termes IGN] traitement du signal Résumé : (Auteur) Hyperspectral unmixing is an important technique for analyzing remote sensing images. In this paper, we consider and examine the ℓq, 0 ≤ q ≤ 1 penalty on the abundances for promoting sparse unmixing of hyperspectral data. We also apply a first-order roughness penalty to promote piecewise smooth end-members. A novel iterative algorithm for simultaneously estimating the end-members and the abundances is developed and tested both on simulated and two real hyperspectral data sets. We present an extensive simulation study where we vary both the SNR and the sparsity of the simulated data and identify the model parameters that minimize the reconstruction errors and the spectral angle distance. We show that choosing 0 ≤ q Numéro de notice : A2014-540 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2303155 En ligne : https://doi.org/10.1109/TGRS.2014.2303155 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74157 [article]

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A discriminative metric learning based anomaly detection method / Bo Du in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 6844 - 6857 Titre : A discriminative metric learning based anomaly detection method Type de document : Article/Communication Auteurs : Bo Du, Auteur ; L. Zhang, Auteur Année de publication : 2014 Article en page(s) : pp 6844 - 6857 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage (cognition) [Termes IGN] détection d'anomalie [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution Résumé : (Auteur) Due to the high spectral resolution, anomaly detection from hyperspectral images provides a new way to locate potential targets in a scene, especially those targets that are spectrally different from the majority of the data set. Conventional Mahalanobis-distance-based anomaly detection methods depend on the background statistics to construct the anomaly detection metric. One of the main problems with these methods is that the Gaussian distribution assumption of the background may not be reasonable. Furthermore, these methods are also susceptible to contamination of the conventional background covariance matrix by anomaly pixels. This paper proposes a new anomaly detection method by effectively exploiting a robust anomaly degree metric for increasing the separability between anomaly pixels and other background pixels, using discriminative information. First, the manifold feature is used so as to divide the pixels into the potential anomaly part and the potential background part. This procedure is called discriminative information learning. A metric learning method is then performed to obtain the robust anomaly degree measurements. Experiments with three hyperspectral data sets reveal that the proposed method outperforms other current anomaly detection methods. The sensitivity of the method to several important parameters is also investigated. Numéro de notice : A2014-541 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2303895 En ligne : https://doi.org/10.1109/TGRS.2014.2303895 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74158 [article]

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A new sparse source separation-based classification approach / M.A. Loghmari in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 6924 - 6936 Titre : A new sparse source separation-based classification approach Type de document : Article/Communication Auteurs : M.A. Loghmari, Auteur ; Mohamed Saber Naceur, Auteur ; Mohamed-Rached Boussema, Auteur Année de publication : 2014 Article en page(s) : pp 6924 - 6936 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification [Termes IGN] classification non dirigée [Termes IGN] extraction de traits caractéristiques [Termes IGN] séparation aveugle de source [Termes IGN] traitement du signal Résumé : (Auteur) In many geoscience applications, we have to convert remotely sensed images to ground cover maps. Numerous approaches to extract ground cover information have been developed. Recently, blind source separation (BSS) of remote-sensing data has received significant attention due to its suitability to recover sources when no information is available about the scanned zone, hence the term blind. In the remote-sensing context, associating each source to a significant land cover theme is difficult and constitutes the real challenge of this paper. Many authors have pointed out that BSS is overwhelmingly a question of contrast and diversity. This reasoning motivates this work which takes advantage of both decorrelation and sparsity to propose a two-level novel approach to separate our different land covers called sources. The first separation stage is based on second-order statistics or decorrelation. It gives a suitable representation of the remote-sensing images. However, decorrelation is a natural way of differentiating statistically between sources but is unable to identify and extract finer features with physical meaning. The aim of the second separation stage is to overcome this problem by an increasingly popular and powerful assumption which is the sparse representation. The last leads to good separation because most of the energy in the defined basis, at any time instant, belongs to a single source. This allows the extraction of physical features and the capture of image essential structures. The innovative aspect of this study concerns the development of a new image classification approach that integrates the BSS at the feature extraction level to provide the most relevant sources from remotely sensed images. It can be viewed as an unsupervised classification method. The second-order separation process is used as a preprocessing step to remove the interband correlation which sometimes brings ill effect to image classification. However, the second-order process is unable to uncover the underlying sources. The basic idea behind our approach is that heterogeneous multichannel data provide sparse spectral signatures in addition to sparse spatial morphologies in specified dictionaries. Hence, sparse modeling can be used to disentangle the land covers from observed mixtures. From the sparse representation, the data space is transformed into a feature space composed of mutually exclusive classes. Finally, we will merge these classes at the decision level in order to enhance the semantic capability and the reliability of land cover classification. The effectiveness of the proposed approach was demonstrated by operating two experiments to study respectively the source separation and the image classification capability of the developed approach. The different results on remote-sensing images illustrate the good performance of the new sparse approach and its robustness to noise. These experiments show that the sparse representation enhances the separation quality and allows extracting more easily the essential structures of the scanned zone. The proposed approach offers an interesting solution to the classification process with limited knowledge of ground truth. Numéro de notice : A2014-542 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2305724 En ligne : https://doi.org/10.1109/TGRS.2014.2305724 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74159 [article]

Exemplaires(1)

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065-2014111A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning / X. Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 7086 - 7098 Titre : Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning Type de document : Article/Communication Auteurs : X. Li, Auteur ; H. Shen, Auteur ; L. Zhang, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 7086 - 7098 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage (cognition) [Termes IGN] détection d'ombre [Termes IGN] réflectance [Termes IGN] température de surface Résumé : (Auteur) With regard to quantitative remote sensing products in the visible and infrared ranges, thick clouds and accompanying shadows are an inevitable source of noise. Due to the absence of adequate supporting information from the data themselves, it is a formidable challenge to accurately restore the surficial information underlying large-scale clouds. In this paper, dictionary learning is expanded into the multitemporal recovery of quantitative data contaminated by thick clouds and shadows. This paper proposes two multitemporal dictionary learning algorithms, expanding on their KSVD and Bayesian counterparts. In order to make better use of the temporal correlations, the expanded KSVD algorithm seeks an optimized temporal path, and the expanded Bayesian method adaptively weights the temporal correlations. In the experiments, the proposed algorithms are applied to a reflectance product and a land surface temperature product, and the respective advantages of the two algorithms are investigated. The results show that, from both the qualitative visual effect and the quantitative objective evaluation, the proposed methods are effective. Numéro de notice : A2014-543 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2307354 En ligne : https://doi.org/10.1109/TGRS.2014.2307354 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74160 [article]

Exemplaires(1)

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065-2014111A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A fast volume-gradient-based band selection method for hyperspectral image / X. Geng in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 11 tome 1 (November 2014) . - pp 7111 - 7119 Titre : A fast volume-gradient-based band selection method for hyperspectral image Type de document : Article/Communication Auteurs : X. Geng, Auteur ; Kang Sun, Auteur ; L. Ji, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 7111 - 7119 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] gradient [Termes IGN] image hyperspectrale [Termes IGN] volume (grandeur) Résumé : (Auteur) In this paper, a subtle relationship is found between the volume of a subsimplex and the volume gradient of a simplex with respect to hyperspectral images. By using this relationship, we propose an efficient band selection method, namely, the volume-gradient-based band selection (VGBS) method. The VGBS method is an unsupervised method, which tries to remove the most redundant band successively. Interestingly, the VGBS method can find the most redundant band based only on the gradient of volume instead of calculating the volumes of all subsimplexes. Experiments on simulated and real hyperspectral data verify the efficiency of the proposed method. Numéro de notice : A2014-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2307880 En ligne : https://doi.org/10.1109/TGRS.2014.2307880 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74162 [article]

Exemplaires(1)

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065-2014111A	RAB	Revue	Centre de documentation	En réserve L003	Disponible