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Border vector detection and adaptation for classification of multispectral and hyperspectral remote sensing images / N.G. Kasapoglu in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)
[article]
Titre : Border vector detection and adaptation for classification of multispectral and hyperspectral remote sensing images Type de document : Article/Communication Auteurs : N.G. Kasapoglu, Auteur ; O.K. Ersoy, Auteur Année de publication : 2007 Article en page(s) : pp 3880 - 3893 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] précision de la classificationRésumé : (Auteur) Effective partitioning of the feature space for high classification accuracy with due attention to rare class members is often a difficult task. In this paper, the border vector detection and adaptation (BVDA) algorithm is proposed for this purpose. The BVDA consists of two parts. In the first part of the algorithm, some specially selected training samples are assigned as initial reference vectors called border vectors. In the second part of the algorithm, the border vectors are adapted by moving them toward the decision boundaries. At the end of the adaptation process, the border vectors are finalized. The method next uses the minimum distance to border vector rule for classification. In supervised learning, the training process should be unbiased to reach more accurate results in testing. In the BVDA, decision region borders are related to the initialization of the border vectors and the input ordering of the training samples. Consensus strategy can be applied with cross validation to reduce these dependencies. The performance of the BVDA and consensual BVDA were studied in comparison to other classification algorithms including neural network with backpropagation learning, support vector machines, and some statistical classification techniques. Copyright IEEE Numéro de notice : A2007-582 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.900699 En ligne : https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4378538 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28945
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 12 Tome 1 (December 2007) . - pp 3880 - 3893[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-07121A RAB Revue Centre de documentation En réserve L003 Disponible Measuring land development in urban regions using graph theoretical and conditional statistical features / C. Unsalan in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)
[article]
Titre : Measuring land development in urban regions using graph theoretical and conditional statistical features Type de document : Article/Communication Auteurs : C. Unsalan, Auteur Année de publication : 2007 Article en page(s) : pp 3989 - 3999 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de graphes
[Termes IGN] extraction automatique
[Termes IGN] graphe
[Termes IGN] image à résolution métrique
[Termes IGN] image Ikonos
[Termes IGN] image panchromatique
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] segment de droite
[Termes IGN] surveillance de l'urbanisationRésumé : (Auteur) Inferring land use from satellite images is extensively studied by the remote sensing and pattern recognition communities. In previous studies, the focus was on classifying large regions due to the resolution of available satellite images. Nowadays, very high-resolution satellite imagery (Ikonos and Quickbird) allows researchers to focus on more complex land-use problems such as monitoring development in urban regions. Solutions to these complex problems may improve the life standards of city residents. To this end, we focus on automatically monitoring construction zones using their very high-resolution panchromatic satellite images through time. To monitor land development, we obtain sequential images of a selected region. Then, we extract features from each image in the sequence. Comparing values of these features, we expect to measure the degree of land development through time. In a similar study, we introduced graph theoretical measures over Ikonos imagery to measure organization in a given satellite image. This paper is an extension of our previous work with more powerful new features. Here, we first introduce a novel method to extract straight line segments using a least squares ellipse fitting. Then, we introduce four new graph theoretical features. More importantly, we introduce a novel method to embed the spatial information in gray-level co-occurrence matrix statistical features to measure land development. Finally, we test all our existing and new features to measure land development in 19 different urban construction zones. Our test set consists of Ikonos satellite images of these regions captured in separate times. Copyright IEEE Numéro de notice : A2007-586 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.897446 En ligne : https://doi.org/10.1109/TGRS.2007.897446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28949
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 12 Tome 1 (December 2007) . - pp 3989 - 3999[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-07121A RAB Revue Centre de documentation En réserve L003 Disponible N-FindR method versus independent component analysis for lithological identification in hyperspectral imagery / C. Gomez in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)
[article]
Titre : N-FindR method versus independent component analysis for lithological identification in hyperspectral imagery Type de document : Article/Communication Auteurs : C. Gomez, Auteur ; H. Le Borgne, Auteur ; P. Allemand, Auteur ; C. Delacourt, Auteur ; P. Ledru, Auteur Année de publication : 2007 Article en page(s) : pp 5315 - 5338 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] classification automatique
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] lithologie
[Termes IGN] méthode robuste
[Termes IGN] Namibie
[Termes IGN] photo-interprétation assistée par ordinateurRésumé : (Auteur) The current study addresses the problem of the identification of each natural material present in each pixel of a hyperspectral image. Two end member extraction methods from hyperspectral imagery were studied: the N-FindR method and the independent component analysis (ICA). The N-FindR is an automatic technique that selects extreme points (end members) of an n-dimensional scatter plot. It assumes the existence of pure pixels in the distribution, which is infrequent in practice. ICA is a blind source separation technique studied in the signal processing community, which allows each spectrum of natural elements (end member spectra) to be extracted from the observation of some linear combinations of these. It considers a more realistic situation than N-FindR, assuming a spectra mixture for all the pixels. To increase the robustness of ICA, continuum-removed reflectance spectra were used and an iterative algorithm was introduced that takes into account a major part of the available information. The end member abundances were estimated by the fully constrained least squares spectral mixture analysis (FLCS). The end member identification and quantification were carried out on two surficial formations of a semi arid region located in the Rehoboth region, in Namibia, from hyperspectral Hyperion data. It appears that the two end member extraction methods have a similar potential. Whichever end member extraction method is used, the analysis of the rock abundance maps produces a lot of geological information: the distribution of natural elements is in line with the field observations and allows the description of the formation processes of surficial units. Copyright Taylor & Francis Numéro de notice : A2007-536 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701227679 En ligne : https://doi.org/10.1080/01431160701227679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28899
in International Journal of Remote Sensing IJRS > vol 28 n°23-24 (December 2007) . - pp 5315 - 5338[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-07131 RAB Revue Centre de documentation En réserve L003 Disponible Seasonal sensitivity analysis of impervious surface estimation with satellite imagery / C. Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 12 (December 2007)
[article]
Titre : Seasonal sensitivity analysis of impervious surface estimation with satellite imagery Type de document : Article/Communication Auteurs : C. Wu, Auteur ; F. Yuan, Auteur Année de publication : 2007 Article en page(s) : pp 1393 - 1401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse de sensibilité
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] Ohio (Etats-Unis)
[Termes IGN] régression
[Termes IGN] surface imperméable
[Termes IGN] variation saisonnièreRésumé : (Auteur) Numerous approaches have been developed to quantify the distribution of impervious surfaces using remote sensing technologies. Most of these approaches have been applied to data from a single time period, typically in the summer season (June to September). Presently, it is not clear whether there is an optimal time for impervious surface estimation with these methods. In this paper, the seasonal sensitivity of impervious surface estimation is examined. In particular, Landsat TM/ETM+ imagery for four different seasons has been acquired for the environs of Franklin County, Ohio. Two impervious surface estimation methods, spectral mixture analysis and regression modeling, are used to test for seasonal variations. Results indicate that the summer image provides better accuracy with the spectral mixture analysis method, while consistent accuracies are obtained for all four seasons with regression modeling. Copyright ASPRS Numéro de notice : A2007-543 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.12.1393 En ligne : https://doi.org/10.14358/PERS.73.12.1393 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28906
in Photogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 12 (December 2007) . - pp 1393 - 1401[article]A time-efficient method for anomaly detection in hyperspectral images / O. Duran in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)
[article]
Titre : A time-efficient method for anomaly detection in hyperspectral images Type de document : Article/Communication Auteurs : O. Duran, Auteur ; M. Petrou, Auteur Année de publication : 2007 Article en page(s) : pp 3894 - 3918 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de Kohonen
[Termes IGN] classification ISODATA
[Termes IGN] détection d'anomalie
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'imageRésumé : (Auteur) We propose a computationally efficient method for determining anomalies in hyperspectral data. In the first stage of the algorithm, the background classes, which are the dominant classes in the image, are found. The method consists of robust clustering of a randomly chosen small percentage of the image pixels. The clusters are the representatives of the background classes. By using a subset of the pixels instead of the whole image, the computation is sped up, and the probability of including outliers in the background model is reduced. Anomalous pixels are the pixels with spectra that have large relative distances from the cluster centers. Several clustering techniques are investigated, and experimental results using realistic hyperspectral data are presented. A self-organizing map clustered using the local minima of the U-matrix (unified distance matrix) is identified as the most reliable method for background class extraction. The proposed algorithm for anomaly detection is evaluated using realistic hyperspectral data, is compared with a state-of-the-art anomaly detection algorithm, and is shown to perform significantly better. Copyright IEEE Numéro de notice : A2007-583 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.909205 En ligne : https://doi.org/10.1109/TGRS.2007.909205 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28946
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 12 Tome 1 (December 2007) . - pp 3894 - 3918[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-07121A RAB Revue Centre de documentation En réserve L003 Disponible Fusing Ikonos images by a four-band wavelet transformation method / Wei Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 11 (November 2007)PermalinkWeighting function alternatives for a subpixel allocation model / Y. Makido in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 11 (November 2007)PermalinkFeature selection by genetic algorithms in object-based classification of Ikonos imagery for forest mapping in Flanders, Belgium / F.M.B. Van Coillie in Remote sensing of environment, vol 110 n° 4 (30/10/2007)PermalinkThe impact of relative radiometric calibration on the accuracy of kNN-predictions of forest attributes / T. Koukal in Remote sensing of environment, vol 110 n° 4 (30/10/2007)PermalinkMultispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation / A. Agrawal in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)PermalinkOptimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition / Jing Tian in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)PermalinkA rough set approach to the discovery of classification rules in spatial data / Yee Leung in International journal of geographical information science IJGIS, vol 21 n° 9-10 (october 2007)PermalinkConstruction et intégration de maquettes 3D dans un SIG / M. Koehl in Géomatique expert, n° 58 (01/09/2007)PermalinkDetection and substitution of clouds/hazes and their cast shadows on Ikonos images / Dong Lu in International Journal of Remote Sensing IJRS, vol 28 n°17-18 (September 2007)PermalinkEstimation of vegetation parameter for modelling soil erosion using linear spectral mixture analysis of Landsat ETM data / A.M. DE Asis in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)PermalinkIntegration of Ikonos and Quickbird imagery for geopositioning accuracy analysis / R. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 9 (September 2007)PermalinkLa morphologie mathématique binaire pour l'extraction automatique des bâtiments dans les images THRS / David Sheeren in Revue internationale de géomatique, vol 17 n° 3-4 (septembre 2007 – février 2008)PermalinkMultispectral images fusion by a joint multidirectional and multiresolution representation / M. Lillo-Saaverda in International Journal of Remote Sensing IJRS, vol 28 n°17-18 (September 2007)PermalinkSensibilité des indices de diversité à l'agrégation / I. Mahfoud in Revue internationale de géomatique, vol 17 n° 3-4 (septembre 2007 – février 2008)PermalinkWavelet based image fusion techniques: an introduction, review and comparison / Krista Amolins in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)PermalinkAssessing alternatives for modelling the spatial distribution of multiple land-cover classes at sub-pixel scales / Y. Makido in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 8 (August 2007)PermalinkMultitemporel fuzzy classification model based on class transition possibilities / G.L.A. Mota in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 3 (August 2007)PermalinkRule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping / Robert Lucas in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 3 (August 2007)PermalinkSpatio-temporal urban landscape change analysis using the Markov chain model and a modified genetic algorithm / J. Tang in International Journal of Remote Sensing IJRS, vol 28 n°15-16 (August 2007)PermalinkSpectral properties and reflectance curves of the revealed volcanic rocks in Syria using radiometric measurements / M. Rukieh in International Journal of Remote Sensing IJRS, vol 28 n°15-16 (August 2007)Permalink