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Semisupervised dual-geometric subspace projection for dimensionality reduction of hyperspectral image data / Shuyuan Yang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 2 (June 2014) . - pp 3587 - 3593 Titre : Semisupervised dual-geometric subspace projection for dimensionality reduction of hyperspectral image data Type de document : Article/Communication Auteurs : Shuyuan Yang, Auteur ; Penglei Jin, Auteur ; Bin Li, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 3587 - 3593 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification semi-dirigée [Termes IGN] image hyperspectrale [Termes IGN] matrice [Termes IGN] réduction géométrique Résumé : (Auteur) Exploring the geometric prior in the dimensionality reduction (DR) of hyperspectral image data (HID) is an important issue because it can overcome the possible overclassification of spectrally homogeneous areas in the HID classification. In this paper, the local geometric similarity of hyperspectral vectors is explored in both the manifold domain and image domain, and a semisupervised dual-geometric subspace projection (DGSP) approach is proposed for the DR of HID, by utilizing both labeled and unlabeled samples. First, the geometric information in the manifold domain is captured by a sparse coding-based geometric graph, and then, a local-consistency-constrained geometric matrix is defined to reveal the geometric structure in the image domain. Second, unlabeled samples are used to refine the geometric structure by defining a pairwise similarity matrix. Third, three scatter matrices are then derived from these similarity matrices to find the optimal subspace projection that captures the most important properties of the subspaces with respect to classification. Some experiments are taken on the airborne visible infrared imaging spectrometer (AVIRIS) HID to prove the efficiency of the proposed method. Numéro de notice : A2014-312 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2273798 En ligne : https://doi.org/10.1109/TGRS.2013.2273798 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33215 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014061B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification / Weiwei Sun in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 89 (March 2014) . - pp 25 - 36 Titre : UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Weiwei Sun, Auteur ; Avner Halevy, Auteur ; John J. Benedetto, Auteur ; Chun Liu, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 25 - 36 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte isoplèthe [Termes IGN] classification barycentrique [Termes IGN] graphe [Termes IGN] image hyperspectrale [Termes IGN] isoligne [Termes IGN] point de repère [Termes IGN] précision de la classification [Termes IGN] réduction géométrique [Termes IGN] valeur propre Résumé : (Auteur) The paper proposes an upgraded landmark-Isometric mapping (UL-Isomap) method to solve the two problems of landmark selection and computational complexity in dimensionality reduction using Landmark Isometric mapping (LIsomap) for hyperspectral imagery (HSI) classification. First, the vector quantization method is introduced to select proper landmarks for HSI data. The approach considers the variations in local density of pixels in the spectral space. It locates the unique landmarks representing the geometric structures of HSI data. Then, random projections are used to reduce the bands of HSI data. After that, the new method incorporates the Recursive Lanczos Bisection (RLB) algorithm to construct the fast approximate k-nearest neighbor graph. The RLB algorithm accompanied with random projections improves the speed of neighbor searching in UL-Isomap. After constructing the geodesic distance graph between landmarks and all pixels, the method uses a fast randomized low-rank approximate method to speed up the eigenvalue decomposition of the inner-product matrix in multidimensional scaling. Manifold coordinates of landmarks are then computed. Manifold coordinates of non-landmarks are computed through the pseudo inverse transformation of landmark coordinates. Five experiments on two different HSI datasets are run to test the new UL-Isomap method. Experimental results show that UL-Isomap surpasses LIsomap, both in the overall classification accuracy (OCA) and in computational speed, with a speed over 5 times faster. Moreover, the UL-Isomap method, when compared against the Isometric mapping (Isomap) method, obtains only slightly lower OCAs. Numéro de notice : A2014-122 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.12.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.12.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33027 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Hyperspectral image classification using nearest feature line embedding approach / Yang-Lang Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 1 tome 1 (January 2014) . - pp 278 - 287 Titre : Hyperspectral image classification using nearest feature line embedding approach Type de document : Article/Communication Auteurs : Yang-Lang Chang, Auteur ; Jan-Nan Liu, Auteur ; Chin-Chuan Han, Auteur ; Ying-Nong Chen, Auteur Année de publication : 2014 Article en page(s) : pp 278 - 287 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse discriminante [Termes IGN] classification barycentrique [Termes IGN] classification dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] image MASTER [Termes IGN] Indiana (Etats-Unis) [Termes IGN] occupation du sol [Termes IGN] réduction géométrique Résumé : (Auteur) Eigenspace projection methods are widely used for feature extraction from hyperspectral images (HSI) for the classification of land cover. Projection transformation is used to reduce higher dimensional feature vectors to lower dimensional vectors for more accurate classification of land cover types. In this paper, a nearest feature line embedding (NFLE) transformation is proposed for the dimension reduction (DR) of an HSI. The NFL measurement is embedded in the transformation during the discriminant analysis phase, instead of the matching phase. Three factors, including class separability, neighborhood structure preservation, and NFL measurement, are considered simultaneously to determine an effective and discriminating transformation in the eigenspaces for land cover classification. Three state-of-the-art classifiers, the nearest-neighbor, support vector machine, and NFL classifiers, were used to classify the reduced features. The proposed NFLE transformation is compared with different feature extraction approaches and evaluated using two benchmark data sets, the MASTER set at Au-Ku and the AVIRIS set at Northwest Tippecanoe County. The experimental results demonstrate that the NFLE approach is effective for DR in land cover classification in the field of Earth remote sensing. Numéro de notice : A2014-036 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2238635 En ligne : https://doi.org/10.1109/TGRS.2013.2238635 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32941 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014011A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Pixel unmixing in hyperspectral data by means of neural networks / Giorgio Licciardi in IEEE Transactions on geoscience and remote sensing, vol 49 n° 11 Tome 1 (November 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 11 Tome 1 (November 2011) . - pp 4163 - 4172 Titre : Pixel unmixing in hyperspectral data by means of neural networks Type de document : Article/Communication Auteurs : Giorgio Licciardi, Auteur ; F. Del Frate, Auteur Année de publication : 2011 Article en page(s) : pp 4163 - 4172 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse en composantes principales [Termes IGN] classification par réseau neuronal [Termes IGN] image AHS [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] image PROBA-CHRIS [Termes IGN] réduction géométrique [Termes IGN] test de performance Résumé : (Auteur) Neural networks (NNs) are recognized as very effective techniques when facing complex retrieval tasks in remote sensing. In this paper, the potential of NNs has been applied in solving the unmixing problem in hyperspectral data. In its complete form, the processing scheme uses an NN architecture consisting of two stages: the first stage reduces the dimension of the input vector, while the second stage performs the mapping from the reduced input vector to the abundance percentages. The dimensionality reduction is performed by the so-called autoassociative NNs, which yield a nonlinear principal component analysis of the data. The evaluation of the whole performance is carried out for different sets of experimental data. The first one is provided by the Airborne Hyperspectral Scanner. The second set consists of images from the Compact High-Resolution Imaging Spectrometer on board the Project for On-Board Autonomy satellite, and it includes multiangle and multitemporal acquisitions. The third set is represented by Airborne Visible/InfraRed Imaging Spectrometer measurements. A quantitative performance analysis has been carried out in terms of effectiveness in the dimensionality reduction phase and in terms of the accuracy in the final estimation. The results obtained, when compared with those produced by appropriate benchmark techniques, show the advantages of this approach. Numéro de notice : A2011-445 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2160950 Date de publication en ligne : 01/08/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2160950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31223 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2011111A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Training set size requirements for the classification of a specific class / Giles M. Foody in Remote sensing of environment, vol 104 n° 1 (15/09/2006)  

Public [article]  inRemote sensing of environment > vol 104 n° 1 (15/09/2006) . - pp 1 - 14 Titre : Training set size requirements for the classification of a specific class Type de document : Article/Communication Auteurs : Giles M. Foody, Auteur ; A. Mathur, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 1 - 14 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] Gossypium (genre) [Termes IGN] Inde [Termes IGN] intelligence artificielle [Termes IGN] réduction géométrique Résumé : (Auteur) The design of the training stage of a supervised classification should account for the properties of the classifier to be used. Consideration of the way the classifier operates may enable the training stage to be designed in a manner which ensures that the aim of the classification is satisfied with the use of a small, inexpensive, training set. It may, therefore, be possible to reduce the training set size requirements from that generally expected with the use of standard heuristics. Substantial reductions in training set size may be possible if interest is focused on a single class. This is illustrated for mapping cotton in north-western India by support vector machine type classifiers. Four approaches to reducing training set size were used: intelligent selection of the most informative training samples, selective class exclusion, acceptance of imprecise descriptions for spectrally distinct classes and the adoption of a one-class classifier. All four approaches were able to reduce the training set size required considerably below that suggested by conventional widely used heuristics without significant impact on the accuracy with which the class of interest was classified. For example, reductions in training set size of not, vert, similar 90% from that suggested by a conventional heuristic are reported with the accuracy of cotton classification remaining nearly constant at not, vert, similar 95% and not, vert, similar 97% from the user's and producer's perspectives respectively. Copyright Elsevier Numéro de notice : A2006-392 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.03.004 En ligne : https://doi.org/10.1016/j.rse.2006.03.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28116 [article]

Trajectory indexing using movement constraints / C.S. Jensen in Geoinformatica, vol 9 n° 2 (June - August 2005)  

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Nonparametric weighted feature extraction for classification / D.A. Landgrebe in IEEE Transactions on geoscience and remote sensing, vol 42 n° 5 (May 2004)  

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Note sur le calcul des erreurs dans les réductions des distances / Abdelmajid Ben Hadj Salem (2002) 

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Geometric reduction of measured lines / T. Vincenty in Surveying and Mapping, vol 46 n° 3 (01/09/1986)

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