Centre de documentation
scientifique

Hyperspectral dimensionality reduction for biophysical variable statistical retrieval / Juan Pablo Rivera-Caicedo in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 88 - 101 Titre : Hyperspectral dimensionality reduction for biophysical variable statistical retrieval Type de document : Article/Communication Auteurs : Juan Pablo Rivera-Caicedo, Auteur ; Jochem Verrelst, Auteur ; Jordi Munoz-Mari, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 88 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] image HYMAP [Termes IGN] image hyperspectrale [Termes IGN] Leaf Area Index [Termes IGN] régression linéaire [Termes IGN] variable biophysique (végétation) Résumé : (Auteur) Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to retrieve leaf area index (LAI): (1) a simulated PROSAIL reflectance data (2101 bands), and (2) a field dataset from airborne HyMap data (125 bands). For the majority of MLRAs, applying first a DR method leads to superior retrieval accuracies and substantial gains in processing speed as opposed to using all bands into the regression algorithm. This was especially noticeable for the PROSAIL dataset: in the most extreme case, using the classical linear regression (LR), validation results (RMSECV) improved from 0.06 (12.23) without a DR method to 0.93 (0.53) when combining it with a best performing DR method (i.e., CCA or OPLS). However, these DR methods no longer excelled when applied to noisy or real sensor data such as HyMap. Then the combination of kernel CCA (KCCA) with LR, or a classical PCA and PLS with a MLRA showed more robust performances ( of 0.93). Gaussian processes regression (GPR) uncertainty estimates revealed that LAI maps as trained in combination with a DR method can lead to lower uncertainties, as opposed to using all HyMap bands. The obtained results demonstrated that, in general, biophysical variable retrieval from hyperspectral data can largely benefit from dimensionality reduction in both accuracy and computational efficiency. Numéro de notice : A2017-640 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86995 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017101	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017102	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2017103	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Semisupervised classification of remote sensing images with active queries / Jordi Munoz-Mari in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 1 (October 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 10 Tome 1 (October 2012) . - pp 3751 - 3763 Titre : Semisupervised classification of remote sensing images with active queries Type de document : Article/Communication Auteurs : Jordi Munoz-Mari, Auteur ; Devis Tuia, Auteur ; G. Camps-Valls, Auteur Année de publication : 2012 Article en page(s) : pp 3751 - 3763 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] apprentissage (cognition) [Termes IGN] carte d'occupation du sol [Termes IGN] carte de confiance [Termes IGN] classification semi-dirigée [Termes IGN] image multibande [Termes IGN] occupation du sol [Termes IGN] requête spatiale [Termes IGN] télédétection spatiale Résumé : (Auteur) We propose a semiautomatic procedure to generate land cover maps from remote sensing images. The proposed algorithm starts by building a hierarchical clustering tree, and exploits the most coherent pixels with respect to the available class information. For a given amount of labeled pixels, the algorithm returns both classification and confidence maps. Since the quality of the map depends of the number and informativeness of the labeled pixels, active learning methods are used to select the most informative samples to increase confidence in class membership. Experiments on four different data sets, accounting for hyperspectral and multispectral images at different spatial resolutions, confirm the effectiveness of the proposed approach, and how active learning techniques reduce the uncertainty of the classification maps. Specifically, more accurate results with fewer labeled samples are obtained. Inclusion of spatial information in the classifiers drastically improves the classification accuracy, leading to faster convergence curves and tighter confidence intervals. In conclusion, the presented algorithm provides efficient image classification and, at the same time, yields a confidence map that may be very useful in many Earth observation applications. Numéro de notice : A2012-524 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2185504 Date de publication en ligne : 08/03/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2185504 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31970 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012101A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Semisupervised one-class support vector machine for classification of remote sensing data / Jordi Munoz-Mari in IEEE Transactions on geoscience and remote sensing, vol 48 n° 8 (August 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 8 (August 2010) . - pp 3188 - 3197 Titre : Semisupervised one-class support vector machine for classification of remote sensing data Type de document : Article/Communication Auteurs : Jordi Munoz-Mari, Auteur ; Francesca Bovolo, Auteur ; et al., Auteur Année de publication : 2010 Article en page(s) : pp 3188 - 3197 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage semi-dirigé [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification semi-dirigée [Termes IGN] détection de changement [Termes IGN] détection de cible Résumé : (Auteur) This paper presents two semisupervised one-class support vector machine (OC-SVM) classifiers for remote sensing applications. In one-class image classification, one tries to detect pixels belonging to one of the classes in the image and reject the others. When few labeled pixels of only one class are available, obtaining a reliable classifier is a difficult task. In the particular case of SVM-based classifiers, this task is even harder because the free parameters of the model need to be finely adjusted, but no clear criterion can be adopted. In order to improve the OC-SVM classifier accuracy and alleviate the problem of free-parameter selection, the information provided by unlabeled samples present in the scene can be used. In this paper, we present two state-of-the-art algorithms for semi-supervised one-class classification for remote sensing classification problems. The first proposed algorithm is based on modifying the OC-SVM kernel by modeling the data marginal distribution with the graph Laplacian built with both labeled and unlabeled samples. The second one is based on a simple modification of the standard SVM cost function which penalizes more the errors made when classifying samples of the target class. The good performance of the proposed methods is illustrated in four challenging remote sensing image classification scenarios where the goal is to detect one of the classes present on the scene. In particular, we present results for multisource urban monitoring, hyperspectral crop detection, multispectral cloud screening, and change-detection problems. Experimental results show the suitability of the proposed techniques, particularly in cases with few or poorly representative labeled samples. Numéro de notice : A2010-307 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2045764 En ligne : https://doi.org/10.1109/TGRS.2010.2045764 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30501 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2010081	RAB	Revue	Centre de documentation	En réserve L003	Disponible