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scientifique

Directionally adaptive filter for synthetic aperture radar interferometric phase images / S. Fu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 2 (January 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 2 (January 2013) . - pp 552 - 559 Titre : Directionally adaptive filter for synthetic aperture radar interferometric phase images Type de document : Article/Communication Auteurs : S. Fu, Auteur ; X. Long, Auteur ; X. Yang, Auteur ; Q. Yu, Auteur Année de publication : 2013 Article en page(s) : pp 552 - 559 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] appariement de formes [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] phase Résumé : (Auteur) The obvious directionality inherent in interferometric synthetic aperture radar interferograms allows noise to be filtered very effectively along the fringe direction, leaving the fringe phase undamaged. This paper proposes a highly reliable method for the simultaneous estimation of the direction and density of interferograms, on the basis of which a directionally adaptive filter (DAF) is further proposed. Compared with existing filters, the DAF has a filter window whose direction is able to vary continuously with the fringe direction. The window length and width can be varied adaptively with the fringe density: a large window achieves better filtering results when the fringes are sparse, whereas a small window is better able to preserve the phase detail when they are dense. The processing results of both simulated and real data demonstrate that the DAF effectively eliminates noise and preserves detailed fringe information because its filter window performs adaptively in terms of both direction and size. Numéro de notice : A2013-017 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2202911 En ligne : https://doi.org/10.1109/TGRS.2012.2202911 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32155 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013011B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An assessment of internal neural network parameters affecting image classification accuracy / L. Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 12 (December 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 12 (December 2011) . - pp 1233 - 1240 Titre : An assessment of internal neural network parameters affecting image classification accuracy Type de document : Article/Communication Auteurs : L. Zhou, Auteur ; X. Yang, Auteur Année de publication : 2011 Article en page(s) : pp 1233 - 1240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal [Termes IGN] image Landsat-ETM+ [Termes IGN] Perceptron multicouche [Termes IGN] précision de la classification [Termes IGN] précision des données Résumé : (Auteur) Neural networks are attractive intelligence techniques increasingly being used to classify remote sensor imagery. However, their performance is contingent upon a wide range of algorithm and non-algorithm factors. Despite significant progresses being made over the past two decades, there is no consistent guidance that has been established to automate the use of neural networks in remote sensing. The purpose of this study was to assess several internal parameters affecting image classification accuracy by multi-layer-perceptron (mlp) neural networks. The MLP networks have been considered as the most popular neural network architecture. We carefully configured and trained a set of neural network models with different internal parameter settings. Then, we used these models to classify an Enhanced Thematic Mapper Plus (ETM+) image into several major land cover categories, and the accuracy of each classified map was assessed. The results reveal that number of hidden layers, activation function, and training rate can substantially affect the classification accuracy and that a neural network with appropriate internal parameters can lead to a significant classification accuracy improvement for urban land covers when comparing to the outcome by the Gaussian Maximum Likelihood (GML) classifier. These findings can help design efficient neural network models for improved performance. Numéro de notice : A2011-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.12.1233 En ligne : https://doi.org/10.14358/PERS.77.12.1233 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31382 [article]

Parameterizing support vector machines for land cover classification / X. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 1 (January 2011) . - pp 27 - 37 Titre : Parameterizing support vector machines for land cover classification Type de document : Article/Communication Auteurs : X. Yang, Auteur Année de publication : 2011 Article en page(s) : pp 27 - 37 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Landsat-TM [Termes IGN] occupation du sol [Termes IGN] séparateur à vaste marge Résumé : (Auteur) The support vector machine is a group of relatively novel statistical learning algorithms that have not been extensively exploited in the remote sensing community. In previous studies they have been found to generally outperform some popular classifiers. Several recent studies found that training samples and input data dimensionalities can affect image classification accuracies by those popular classifiers and support vector machines alike. The current study extends beyond these recent research frameworks and into another important inquiry area addressing the impacts of internal parameterization on the performance of support vector machines for land-cover classification. A set of support vector machines with different combinations of kernel types, parameters, and error penalty are carefully constructed to classify a Landsat Thematic Mapper image into eight major land-cover categories using identical training data. The accuracy of each classified map is further evaluated using identical reference data. The results reveal that kernel types and error penalty can substantially affect the classification accuracy, and that a careful selection of parameter settings can help improve the performance of the support vector classification. These findings reported here can help establish a practical guidance on the use of support vector machines for land-cover classification from remote sensor data. Numéro de notice : A2011-002 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.1.27 En ligne : https://doi.org/10.14358/PERS.77.1.27 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30784 [article]

Using satellite imagery and GIS for land-use and land-cover change mapping in an estuarine watershed / X. Yang in International Journal of Remote Sensing IJRS, vol 26 n° 23 (December 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 23 (December 2005) . - pp 5275 - 5296 Titre : Using satellite imagery and GIS for land-use and land-cover change mapping in an estuarine watershed Type de document : Article/Communication Auteurs : X. Yang, Auteur ; Z. Liu, Auteur Année de publication : 2005 Article en page(s) : pp 5275 - 5296 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse diachronique [Termes IGN] baie [Termes IGN] bassin hydrographique [Termes IGN] carte d'occupation du sol [Termes IGN] carte thématique [Termes IGN] changement d'occupation du sol [Termes IGN] classification [Termes IGN] classification ascendante hiérarchique [Termes IGN] croissance urbaine [Termes IGN] dégradation de l'environnement [Termes IGN] écosystème [Termes IGN] estuaire [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] littoral [Termes IGN] Mexique (golfe du) [Termes IGN] précision de la classification [Termes IGN] protection de l'environnement [Termes IGN] système d'information géographique Résumé : (Auteur) The degradation of world-wide estuarine ecosystems as a result of accelerated human population growth accompanied by agricultural, industrial and urban development justifies a strong need to find efficient ways to manage and protect these sensitive environments. Starting from 2001. the authors have been involved in an interdisciplinary research project aiming to develop environmental indicators for integrated estuarine ecosystem assessment in the Gulf of Mexico. As part of this project, a study was conducted to characterize land-use and land-cover changes with the Pensacola estuarine drainage area as a case. The Pensacola bay was targeted because it is one of few exemplary large river-driven estuarine systems across the northern Gulf of Mexico. The study had two major sections. The first part was dedicated to the development of an improved method for coastal land-use and land-cover mapping, which was built upon hierarchical classification and spatial reclassification. An image scene was separated into urban and rural regions early in the classification, with a 'mask' defined by road intersection density slices combined with road buffers. Each part was classified independently in its most effective context and, later, both were merged to form a complete map. In spatial reclassification, image interpretation procedures, auxiliary vector data and a variety of Geographical Information System (GIS) functions were synthesized to resolve spectral confusion and improve mapping accuracy. This method was used to map land use and land cover from Landsat Thematic Mapper/Enhanced Thernatic Mapper Plus (TM/ETM +) imagery for 1989, 1996 and 2002, respectively. The accuracy assessment shows that the overall classification errors were less than 10%. The second part focused on the analysis of the spatio-temporal dynamics of estuarine land-use and land-cover changes by using post-classification comparison and GIS overlay techniques. The project has revealed that a substantial growth of low-density urban land occurred in the lower drainage basin in connection with population and housing growth, as well as a significant increase of mixed forest land in the upper watershed as a result of active logging and harvesting operations. These growths were achieved at the cost of evergreen forest and wetlands, thus compromising safeguards for water quality, biodiversity of aquatic systems, habitat structure and watershed health in the Pensacola estuarine drainage area. Numéro de notice : A2005-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500219224 En ligne : https://doi.org/10.1080/01431160500219224 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27653 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-05231	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Modelling urban growth and landscape changes in the Atlanta metropolitan area / X. Yang in International journal of geographical information science IJGIS, vol 17 n° 5 (july - August 2003)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 17 n° 5 (july - August 2003) . - pp 463 - 488 Titre : Modelling urban growth and landscape changes in the Atlanta metropolitan area Type de document : Article/Communication Auteurs : X. Yang, Auteur ; C.P. Lo, Auteur Année de publication : 2003 Article en page(s) : pp 463 - 488 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Urbanisme [Termes IGN] Atlanta (Géorgie) [Termes IGN] croissance urbaine [Termes IGN] métropole [Termes IGN] modèle de simulation Résumé : (Auteur) We use an urban growth model, closely coupled with a land transition model, to simulate future urban growth in the Atlanta metropolitan area, one of the fastest growing metropolises in the United States during the past three decades. We calibrate the model with historical data that are extracted from a time series of satellite images. We design three specific scenarios to simulate the spatial consequences of urban growth under different environmental conditions. The first scenario is to simulate the continued growth trend by maintaining unchanged the current conditions. The second scenario is to project the growth trend by taking into consideration road development and environmental protection. The third scenario is to simulate the development trend by slowing down growth and changing growth pattern. The first two scenarios demonstrate that unchecked urban growth would result in the displacement of almost the entire natural vegetation and all of the open space in the metro area. In contrast, the result from the third scenario shows that much more greenness and open space, including buffer zones of large streams and lakes, could be preserved. Accordingly, the last scenario should be the most desirable for the future urban growth of Atlanta. We also examine the model's effectiveness as applied to the Atlanta area and suggest future research directions for more accurate simulations. Numéro de notice : A2003-145 Affiliation des auteurs : non IGN Thématique : URBANISME Nature : Article DOI : 10.1080/1365881031000086965 En ligne : https://doi.org/10.1080/1365881031000086965 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22441 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-03051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-03052	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Multi-band wavelet for using Spot panchromatic and multispectral images / Wei Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 5 (May 2003)  

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