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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)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°23-24 (December 2007) . - pp 5315 - 5338 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 ordinateur Résumé : (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 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07131	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An unbiased algorithm for detection of curvilinear structures in urban remote sensing images / Jinzheng Peng in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°23-24 (December 2007) . - pp 5377 - 5395 Titre : An unbiased algorithm for detection of curvilinear structures in urban remote sensing images Type de document : Article/Communication Auteurs : Jinzheng Peng, Auteur ; Ya-Qiu Jin, Auteur Année de publication : 2007 Article en page(s) : pp 5377 - 5395 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme de Gauss [Termes IGN] courbe [Termes IGN] extraction automatique [Termes IGN] extraction du réseau routier [Termes IGN] image aérienne [Termes IGN] objet géographique linéaire Résumé : (Auteur) Based on the Gaussian scale-space, a Gaussian comparison function is presented for extracting the linearly road features in aerial remote sensing image. Combining the geometric and radiometric features, the curvilinear structures of the roads are extracted based on locally oriented energy in continuous scale-space. Curvilinear features of roads are verified, grouped and extracted by using both topologic and geometric methods. This algorithm is applicable to extracting the road features in different scale such as rural roads or urban highways, and significantly reduces the computation complexity of line tracing. Some discussions on the zero drift of the Gaussian comparison function are also presented. Copyright Taylor & Francis Numéro de notice : A2007-537 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160601075574 En ligne : https://doi.org/10.1080/01431160601075574 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28900 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07131	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Land-cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data / Dong Lu in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°23-24 (December 2007) . - pp 5447 - 5459 Titre : Land-cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data Type de document : Article/Communication Auteurs : Dong Lu, Auteur ; M. Batistella, Auteur ; E. Moran, Auteur Année de publication : 2007 Article en page(s) : pp 5447 - 5459 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] Amazonie [Termes IGN] analyse texturale [Termes IGN] Brésil [Termes IGN] classification par maximum de vraisemblance [Termes IGN] fusion d'images [Termes IGN] image Landsat-ETM+ [Termes IGN] image multibande [Termes IGN] image optique [Termes IGN] image panchromatique [Termes IGN] image radar [Termes IGN] image Radarsat [Termes IGN] niveau de gris (image) [Termes IGN] occupation du sol [Termes IGN] transformation en ondelettes [Termes IGN] zone tropicale humide Résumé : (Auteur) Land-cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet-merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey-level co-occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum-likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land-cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8-6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land-cover classification accuracies. Copyright Taylor & Francis Numéro de notice : A2007-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701227596 En ligne : https://doi.org/10.1080/01431160701227596 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28901 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07131	RAB	Revue	Centre de documentation	En réserve L003	Disponible