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A robust background regression based score estimation algorithm for hyperspectral anomaly detection / Zhao Rui in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)
[article]
Titre : A robust background regression based score estimation algorithm for hyperspectral anomaly detection Type de document : Article/Communication Auteurs : Zhao Rui, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur ; Lefei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 126 – 144 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'anomalie
[Termes IGN] image hyperspectrale
[Termes IGN] régressionRésumé : (Auteur) Anomaly detection has become a hot topic in the hyperspectral image analysis and processing fields in recent years. The most important issue for hyperspectral anomaly detection is the background estimation and suppression. Unreasonable or non-robust background estimation usually leads to unsatisfactory anomaly detection results. Furthermore, the inherent nonlinearity of hyperspectral images may cover up the intrinsic data structure in the anomaly detection. In order to implement robust background estimation, as well as to explore the intrinsic data structure of the hyperspectral image, we propose a robust background regression based score estimation algorithm (RBRSE) for hyperspectral anomaly detection. The Robust Background Regression (RBR) is actually a label assignment procedure which segments the hyperspectral data into a robust background dataset and a potential anomaly dataset with an intersection boundary. In the RBR, a kernel expansion technique, which explores the nonlinear structure of the hyperspectral data in a reproducing kernel Hilbert space, is utilized to formulate the data as a density feature representation. A minimum squared loss relationship is constructed between the data density feature and the corresponding assigned labels of the hyperspectral data, to formulate the foundation of the regression. Furthermore, a manifold regularization term which explores the manifold smoothness of the hyperspectral data, and a maximization term of the robust background average density, which suppresses the bias caused by the potential anomalies, are jointly appended in the RBR procedure. After this, a paired-dataset based k-nn score estimation method is undertaken on the robust background and potential anomaly datasets, to implement the detection output. The experimental results show that RBRSE achieves superior ROC curves, AUC values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods, and is easy to implement in practice. Numéro de notice : A2016--023 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.10.006 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.10.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83886
in ISPRS Journal of photogrammetry and remote sensing > vol 122 (December 2016) . - pp 126 – 144[article]The practical application of 3D vision in the field: Measuring reindeer (rangifer tarandus) antler growth velocities / Derek D. Lichti in Photogrammetric record, vol 31 n° 156 (December 2016 - February 2017)
[article]
Titre : The practical application of 3D vision in the field: Measuring reindeer (rangifer tarandus) antler growth velocities Type de document : Article/Communication Auteurs : Derek D. Lichti, Auteur ; Jeremy Steward, Auteur ; Jacky C. K. Chow, Auteur ; John Matyas, Auteur Année de publication : 2016 Article en page(s) : pp 394 - 406 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] analyse diachronique
[Termes IGN] caméra 3D temps-de-vol
[Termes IGN] image 3D
[Termes IGN] image optique
[Termes IGN] semis de pointsRésumé : (auteur) Advances in three-dimensional (3D) optical imaging have made possible precise and accurate measurements of many scenes, ranging from engineering to architecture to art. However, measurements of some 3D objects are more difficult to obtain than others, particularly if the edges do not feature regular geometry, the colour is dark and variable, and if the object moves haphazardly. Such objects occur regularly in biology, and the present study illustrates some of the challenges of evaluating such objects. The growing antlers of three live reindeer (Rangifer tarandus) is presented as an example of how 3D imaging, specifically time-of-flight range imaging, can be used to solve to a reasonable extent a problem that is very difficult to approximate using traditional techniques. Mean antler growth velocities of the order of 7 to 9 mm/day were estimated, using the proposed methodology, from data of these three animals collected over a seven-week period. Numéro de notice : A2016--003 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12167 Date de publication en ligne : 07/11/2016 En ligne : https://doi.org/10.1111/phor.12167 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83821
in Photogrammetric record > vol 31 n° 156 (December 2016 - February 2017) . - pp 394 - 406[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2016041 RAB Revue Centre de documentation En réserve L003 Disponible Blind hyperspectral unmixing using total variation and ℓq sparse regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
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Titre : Blind hyperspectral unmixing using total variation and ℓq sparse regularization Type de document : Article/Communication Auteurs : Jakob Sigurdsson, Auteur ; Magnus Orn Ulfarsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2016 Article en page(s) : pp 6371 - 6384 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] régularisation d'image
[Termes IGN] simulation d'image
[Termes IGN] variableRésumé : (Auteur) Blind hyperspectral unmixing involves jointly estimating endmembers and fractional abundances in hyperspectral images. An endmember is the spectral signature of a specific material in an image, while an abundance map specifies the amount of a material seen in each pixel in an image. In this paper, a new cyclic descent algorithm for blind hyperspectral unmixing using total variation (TV) and ℓq sparse regularization is proposed. Abundance maps are both spatially smooth and sparse. Their sparsity derives from the fact that each material in the image is not represented in all pixels. The abundance maps are assumed to be piecewise smooth since adjacent pixels in natural images tend to be composed of similar material. The TV regularizer is used to encourage piecewise smooth images, and the ℓq regularizer promotes sparsity. The dyadic expansion decouples the problem, making a cyclic descent procedure possible, where one abundance map is estimated, followed by the estimation of one endmember. A novel debiasing technique is also employed to reduce the bias of the algorithm. The algorithm is evaluated using both simulated and real hyperspectral images. Numéro de notice : A2016-914 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2582824 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2582824 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83136
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6371 - 6384[article]Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection / Jie Feng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
[article]
Titre : Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection Type de document : Article/Communication Auteurs : Jie Feng, Auteur ; Licheng Jiao, Auteur ; Tao Sun, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 6516 - 6530 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification automatique
[Termes IGN] image hyperspectrale
[Termes IGN] intelligence artificielle
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) In hyperspectral images, band selection plays a crucial role for land-cover classification. Multiple kernel learning (MKL) is a popular feature selection method by selecting the relevant features and classifying the images simultaneously. Unfortunately, a large number of spectral bands in hyperspectral images result in excessive kernels, which limit the application of MKL. To address this problem, a novel MKL method based on discriminative kernel clustering (DKC) is proposed. In the proposed method, a discriminative kernel alignment (KA) (DKA) is defined. Traditional KA measures kernel similarity independently of the current classification task. Compared with KA, DKA measures the similarity of discriminative information by introducing the comparison of intraclass and interclass similarities. It can evaluate both kernel redundancy and kernel synergy for classification. Then, DKA-based affinity-propagation clustering is devised to reduce the kernel scale and retain the kernels having high discrimination and low redundancy for classification. Additionally, an analysis of necessity for DKC in hyperspectral band selection is provided by empirical Rademacher complexity. Experimental results on several hyperspectral images demonstrate the effectiveness of the proposed band selection method in terms of classification performance and computation efficiency. Numéro de notice : A2016-915 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2585961 En ligne : https://doi.org/10.1109/TGRS.2016.2585961 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83140
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6516 - 6530[article]Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification / Zhi He in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)
[article]
Titre : Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification Type de document : Article/Communication Auteurs : Zhi He, Auteur ; Lin Liu, Auteur Année de publication : 2016 Article en page(s) : pp 11 – 27 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification
[Termes IGN] décomposition d'image
[Termes IGN] image hyperspectrale
[Termes IGN] module d'extensionRésumé : (Auteur) Empirical mode decomposition (EMD) and its variants have recently been applied for hyperspectral image (HSI) classification due to their ability to extract useful features from the original HSI. However, it remains a challenging task to effectively exploit the spectral-spatial information by the traditional vector or image-based methods. In this paper, a three-dimensional (3D) extension of EMD (3D-EMD) is proposed to naturally treat the HSI as a cube and decompose the HSI into varying oscillations (i.e. 3D intrinsic mode functions (3D-IMFs)). To achieve fast 3D-EMD implementation, 3D Delaunay triangulation (3D-DT) is utilized to determine the distances of extrema, while separable filters are adopted to generate the envelopes. Taking the extracted 3D-IMFs as features of different tasks, robust multitask learning (RMTL) is further proposed for HSI classification. In RMTL, pairs of low-rank and sparse structures are formulated by trace-norm and l1,2l1,2-norm to capture task relatedness and specificity, respectively. Moreover, the optimization problems of RMTL can be efficiently solved by the inexact augmented Lagrangian method (IALM). Compared with several state-of-the-art feature extraction and classification methods, the experimental results conducted on three benchmark data sets demonstrate the superiority of the proposed methods. Numéro de notice : A2016--011 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.007 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.08.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83873
in ISPRS Journal of photogrammetry and remote sensing > vol 121 (November 2016) . - pp 11 – 27[article]Semi-supervised hyperspectral classification from a small number of training samples using a co-training approach / Michał Romaszewski in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkWave period and coastal bathymetry using wave propagation on optical images / Céline Danilo in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkAn operational high-resolution forest inventory / Julianno Sambatti in GIM international, vol 30 n° 10 (October 2016)PermalinkA Computationally efficient algorithm for fusing multispectral and hyperspectral images / Raúl Guerra in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkDeep feature extraction and classification of hyperspectral images based on convolutional neural networks / Yushi Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkEvaluating EO1-Hyperion capability for mapping conifer and broadleaved forests / Nicola Puletti in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkFast 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)PermalinkHabitat 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)PermalinkInfluence of tree species complexity on discrimination performance of vegetation indices / Azadeh Ghiyamat in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkObject-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)PermalinkSemisupervised 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)Permalink