International Journal of Remote Sensing IJRS / Remote sensing and photogrammetry society . vol 26 n° 7Paru le : 10/04/2005 ISBN/ISSN/EAN : 0143-1161 |
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est un bulletin de International Journal of Remote Sensing IJRS / Remote sensing and photogrammetry society (1980 -)
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Code-barres | Cote | Support | Localisation | Section | Disponibilité |
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080-05071 | RAB | Revue | Centre de documentation | En réserve L003 | Exclu du prêt |
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Ajouter le résultat dans votre panierUpdating land cover classification using a rule-based decision system / Damien Raclot in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)
[article]
Titre : Updating land cover classification using a rule-based decision system Type de document : Article/Communication Auteurs : Damien Raclot, Auteur ; F. Colin, Auteur ; C. Puech, Auteur Année de publication : 2005 Article en page(s) : pp 1309 - 1321 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture
[Termes IGN] base de données d'occupation du sol
[Termes IGN] base de règles
[Termes IGN] classification dirigée
[Termes IGN] cultures
[Termes IGN] données auxiliaires
[Termes IGN] Gers (32)
[Termes IGN] image isolée
[Termes IGN] image multibande
[Termes IGN] image SPOT
[Termes IGN] mise à jour
[Termes IGN] occupation du sol
[Termes IGN] parcelle agricole
[Termes IGN] pixel
[Termes IGN] Sousson (rivière)Résumé : (Auteur) This paper proposes a land cover classification methodology in agricultural contexts that provides satisfactory results with a single satellite image per year acquired during the growing period. Our approach incorporates ancillary data such as cropping history (inter-annual crop rotations), context (altitude, soil type) and structure (parcels size and shape) to compensate for the lack of radiometric data resulting from the use of a single image. The originality of the proposed method resides in the three successive steps used: S1: per-pixel classification of a single SPOT XS image with a restricted number of land cover classes (RL) chosen to ensure good accuracy; S2: conversion of RLs into a per-parcel classification system using ancillary parcel boundaries: and S3: parcel allocation using exhaustive land cover classes (EL) and its refinement through the application of decision rules. The method was tested on a 120 km2 area (Sousson river basin, Gers, France) where exhaustive knowledge of land cover for two successive years allowed complete validation of our method. It allocated 87% of the parcels with a 75% accuracy rate according to the exhaustive list (EL). This is a satisfactory result obtained with one SPOT XS image in a small agricultural parcel context. Numéro de notice : A2005-177 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331326774 En ligne : https://doi.org/10.1080/01431160512331326774 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27314
in International Journal of Remote Sensing IJRS > vol 26 n° 7 (April 2005) . - pp 1309 - 1321[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05071 RAB Revue Centre de documentation En réserve L003 Exclu du prêt SPOT-4 Vegetation multi-temporal compositing for land cover change studies over tropical regions / João M.B. Carreiras in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)
[article]
Titre : SPOT-4 Vegetation multi-temporal compositing for land cover change studies over tropical regions Type de document : Article/Communication Auteurs : João M.B. Carreiras, Auteur ; J.M.C. Pereira, Auteur Année de publication : 2005 Article en page(s) : pp 1323 - 1346 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] autocorrélation spatiale
[Termes IGN] cohérence des données
[Termes IGN] détection de changement
[Termes IGN] filtrage du bruit
[Termes IGN] forêt tropicale
[Termes IGN] image multitemporelle
[Termes IGN] image optique
[Termes IGN] image SPOT-Végétation
[Termes IGN] Mato Grosso
[Termes IGN] nébulosité
[Termes IGN] rapport signal sur bruit
[Termes IGN] Soil Adjusted Vegetation Index
[Termes IGN] surveillance agricole
[Termes IGN] utilisation du sol
[Termes IGN] zone intertropicaleRésumé : (Auteur) Multi-temporal compositing of SPOT-4 VEGETATION imagery over tropical regions was tested to produce spatially coherent monthly composite images with reduced cloud contamination, for the year 2000. Monthly composite images generated from daily images (S1 product, 1-km) encompassing different land cover. types of the state of Mato Grosso, Brazil, were evaluated in terms of cloud contamination and spatial consistency. A new multi-temporal compositing algorithm was tested which uses different criteria for vegetated and non-vegetated or sparsely vegetated land cover types. Furthermore, a principal components transformation that rescales the noise in the image-Maximum Noise Fraction (MNF)- was applied to a multi-temporal dataset of monthly composite images and tested as a method of additional signal-to-noise ratio improvement. The back-transformed dataset using the first 12 MNF eigenimages yielded an accurate reconstruction of monthly composite images from the dry season (May to September) and enhanced spatial coherence from wet season images (October to April), as evaluated by the Moran's 1 index of spatial autocorrelation. This approach is useful for land cover- change studies in the tropics, where it is difficult to obtain cloud-free optical remote sensing imagery. In Mato Grosso, wet season composite images are important for monitoring agricultural crop cycles. Numéro de notice : A2005-178 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331338005 En ligne : https://doi.org/10.1080/01431160512331338005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27315
in International Journal of Remote Sensing IJRS > vol 26 n° 7 (April 2005) . - pp 1323 - 1346[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05071 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Land covers update by supervised classification of segmented ASTER images / A.R.S. Marcal in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)
[article]
Titre : Land covers update by supervised classification of segmented ASTER images Type de document : Article/Communication Auteurs : A.R.S. Marcal, Auteur ; J.S. Borges, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 1347 - 1362 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] carte d'occupation du sol
[Termes IGN] classificateur paramétrique
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] classification floue
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] image Terra-ASTER
[Termes IGN] mise à jour cartographique
[Termes IGN] Portugal
[Termes IGN] segmentation d'imageRésumé : (Auteur) The revision of the 1995 land cover dataset for the Vale do Sousa region, in the northwest of Portugal, was carried out by supervised classification of a multispectral image from the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) sensor. The nine reflective bands of ASTER were used, covering the spectral range from 0.52-2.43 um. The image was initially ortho-rectified and segmented into 51 186 objects, with an average object size of 135 pixels (about 3 ha). A total of 582 of these objects were identified for training nine land cover classes. The image was classified using an algorithm based on a fuzzy classifier, Support Vector Machines (SVM), K Nearest Neighbours (K-NN) and a Logistic Discrimination (LD) classifier. The results from the classification were evaluated using a set of 277 validation sites, independently gathered. The overall accuracy was 44.6%, for the fuzzy classifier. 70.5%, for the SVM, 60.9% for the K-NN and 72.2% for the LD classifier. The difficulty in discriminating between some of the forest land cover classes was examined by separability analysis and unsupervised classification with hierarchical clustering. The forest classes were found to overlap in the multi-spectral space defined by the nine ASTER bands used. Numéro de notice : A2005-179 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160412331291233 En ligne : https://doi.org/10.1080/01431160412331291233 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27316
in International Journal of Remote Sensing IJRS > vol 26 n° 7 (April 2005) . - pp 1347 - 1362[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05071 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data / S. Lee in International Journal of Remote Sensing IJRS, vol 26 n° 7 (April 2005)
[article]
Titre : Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data Type de document : Article/Communication Auteurs : S. Lee, Auteur Année de publication : 2005 Article en page(s) : pp 1477 - 1491 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] base de données topographiques
[Termes IGN] données de terrain
[Termes IGN] effondrement de terrain
[Termes IGN] image Landsat-TM
[Termes IGN] image SPOT
[Termes IGN] indice de végétation
[Termes IGN] Malaisie
[Termes IGN] modèle de régression
[Termes IGN] photo-interprétation
[Termes IGN] photographie aérienne
[Termes IGN] prévention des risques
[Termes IGN] risque naturel
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du solRésumé : (Auteur) The aim of this study is to evaluate the hazard of landslide at Penang Malaysia, using a Geographical Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database: land use from Thematic Mapper (TM) satellite images; and the vegetation index value from Systeme Probatoire de l'Observation de la Terre (SPOT) .satellites images. Landslide hazardous areas were analysed and mapped using the landslide-occurrence factors by logistic regression model. The results of the analysis .were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better in prediction than probabilistic model. Numéro de notice : A2005-180 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160412331331012 En ligne : https://doi.org/10.1080/01431160412331331012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27317
in International Journal of Remote Sensing IJRS > vol 26 n° 7 (April 2005) . - pp 1477 - 1491[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05071 RAB Revue Centre de documentation En réserve L003 Exclu du prêt