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Fuzzy rule-based classification of remotely sensed imagery / A. Bardossy in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 40 n° 2 (February 2002) . - pp 362 - 374 Titre : Fuzzy rule-based classification of remotely sensed imagery Type de document : Article/Communication Auteurs : A. Bardossy, Auteur ; L. Samaniego, Auteur Année de publication : 2002 Article en page(s) : pp 362 - 374 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] classification dirigée [Termes IGN] classification floue [Termes IGN] image Landsat [Termes IGN] occupation du sol [Termes IGN] simulation Résumé : (Auteur) The purpose of this paper is to investigate the applicability of fuzzy rule-based modeling to classify a LANDSAT TM scene from 1984 of an area located in the south of Germany. Both a land cover map with four different categories and an image depicting the degree of ambiguity of the classification for each pixel is the expected output. The fuzzy classification algorithm will use a rule system derived from a training set using simulated annealing as an optimization algorithm. The results are then validated and compared with a common classification method in order to judge the effectiveness of the proposed technique. It will also be shown that the proposed method with only nine rules for four different land cover classes performs slightly better than the maximum likelihood classifier (MLC). For error assessment, the traditional error matrix and fuzzy operators have been used. Numéro de notice : A2002-096 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.992798 En ligne : https://doi.org/10.1109/36.992798 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22011 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-02021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-02022	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Linear spectral random mixture analysis for hyperspectral imagery / C.I. Chang in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 40 n° 2 (February 2002) . - pp 375 - 392 Titre : Linear spectral random mixture analysis for hyperspectral imagery Type de document : Article/Communication Auteurs : C.I. Chang, Auteur ; S.S. Chiang, Auteur ; J.A. Smith, Auteur ; I.W. Ginsberg, Auteur Année de publication : 2002 Article en page(s) : pp 375 - 392 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes indépendantes [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] analyse spectrale [Termes IGN] classification non dirigée [Termes IGN] image hyperspectrale Résumé : (Auteur) Independent component analysis (ICA) has shown success in blind source separation and channel equalization. Its applications to remotely sensed images have been investigated in recent years. Linear spectral mixture analysis (LSMA) has been widely used for subpixel detection and mixed pixel classification. It models an image pixel as a linear mixture of materials present in an image where the material abundance fractions are assumed to be unknown and nonrandom parameters. This paper considers an application of ICA to the LSMA, referred to as ICA-based linear spectral random mixture analysis (LSRMA), which describes an image pixel as a random source resulting from a random composition of multiple spectral signatures of distinct materials in the image. It differs from the LSMA in that the abundance fractions of the material spectral signatures in the LSRMA are now considered to be unknown but random independent signal sources. Two major advantages result from the LSRMA. First, it does not require prior knowledge of the materials to be used in the linear mixture model, as required for the LSMA. Second, and most importantly, the LSRMA models the abundance fraction of each material spectral signature as an independent random signal source so that the spectral variability of materials can be described by their corresponding abundance fractions and captured more effectively in a stochastic manner. The experimental results demonstrate that the proposed LSRMA provides an effective unsupervised technique for target detection and image classification in hyperspectral imagery. Numéro de notice : A2002-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.992799 En ligne : https://doi.org/10.1109/36.992799 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22012 [article]

Exemplaires(2)

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065-02021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-02022	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction / N.R. Harvey in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 40 n° 2 (February 2002) . - pp 393 - 404 Titre : Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction Type de document : Article/Communication Auteurs : N.R. Harvey, Auteur ; et al., Auteur Année de publication : 2002 Article en page(s) : pp 393 - 404 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification dirigée [Termes IGN] extraction de traits caractéristiques [Termes IGN] extraction semi-automatique [Termes IGN] image multibande Résumé : (Auteur) We have developed an automated feature detection/classification system, called GENetic Imagery Exploitation (GENIE), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. GENIE is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multispectral remotely-sensed images. We describe our system in detail together with experiments involving comparisons of GENIE with several conventional supervised classification techniques, for a number of classification tasks using multispectral remotely sensed imagery. Numéro de notice : A2002-098 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.992801 En ligne : https://doi.org/10.1109/36.992801 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22013 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-02021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-02022	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Cloud tracking by scale space classification / D.P. Mukherjee in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 40 n° 2 (February 2002) . - pp 405 - 415 Titre : Cloud tracking by scale space classification Type de document : Article/Communication Auteurs : D.P. Mukherjee, Auteur ; S.T. Acton, Auteur Année de publication : 2002 Article en page(s) : pp 405 - 415 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse spatio-temporelle [Termes IGN] analyse structurale [Termes IGN] classification [Termes IGN] détection de contours [Termes IGN] nuage [Termes IGN] séquence d'images Résumé : (Auteur) The problem of cloud tracking within a sequence of geo-stationary satellite images has direct relevance to the analysis of cloud life cycles and to the detection of cloud motion vectors (CMVs). The proposed approach first identifies a homogeneous consistent cloud mass for tracking and then establishes motion correspondence within an image sequence. In contrast to the crosscorrelation based approach as adopted in automatic CNIV detection analysis, a scale space classifier is designed to detect cloud mass in the source image taken at time t and the destination image at time t + dt. Boundaries of the extracted cloud segments are matched by computing a correspondence between high curvature points. This shape based method is capable of tracking in the cases of rotation, scaling, and shearing, while the correlation technique is limited to translational motion. The final tracking results provide motion magnitude and direction for each contour point, allowing reliable estimation of meteorological events and wind velocities aloft. With comparable computational expense, the scale space classification technique exceeds the performance of the traditional correlation-based approach in terms of reduced localization error and false matches. Numéro de notice : A2002-099 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.992803 En ligne : https://doi.org/10.1109/36.992803 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22014 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-02021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-02022	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A derivative-aided hyperspectral image analysis system for land-cover classification / F. Tsai in IEEE Transactions on geoscience and remote sensing, vol 40 n° 2 (February 2002)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 40 n° 2 (February 2002) . - pp 416 - 425 Titre : A derivative-aided hyperspectral image analysis system for land-cover classification Type de document : Article/Communication Auteurs : F. Tsai, Auteur ; W.D. Philpot, Auteur Année de publication : 2002 Article en page(s) : pp 416 - 425 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de données [Termes IGN] classification dirigée [Termes IGN] image hyperspectrale [Termes IGN] occupation du sol Résumé : (Auteur) The large number of spectral bands in hyperspectral data seriously complicates their use for classification. Selection of a useful subset of bands or derived features (spectral ratios, differences, derivatives) is always desirable, strongly affects the accuracy of the classification, and is often a practical necessity to keep the processing speed and memory requirements under control. This paper examines one possible procedure for selecting spectral derivatives to improve supervised classification of hyperspectral images. The procedure is designed to identify derivative features that are more effective at separating target classes and then add them to a base subset of features for classification. The goal is to create the smallest set of features that will result in the best classification result. A key issue in this process is the interplay of the number of features and the size of the training data sets since classification accuracy declines if the dimensionality of the feature space is too large relative to the number of training samples. Numéro de notice : A2002-100 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.992805 En ligne : https://doi.org/10.1109/36.992805 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22015 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-02021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
065-02022	RAB	Revue	Centre de documentation	En réserve L003	Disponible