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Spectral filtering and classification of terrestrial laser scanner point clouds / Derek D. Lichti in Photogrammetric record, vol 20 n° 111 (September - November 2005)  

Public [article]  inPhotogrammetric record > vol 20 n° 111 (September - November 2005) . - pp 218 - 240 Titre : Spectral filtering and classification of terrestrial laser scanner point clouds Type de document : Article/Communication Auteurs : Derek D. Lichti, Auteur Année de publication : 2005 Article en page(s) : pp 218 - 240 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classificateur paramétrique [Termes IGN] classification dirigée [Termes IGN] coefficient de corrélation [Termes IGN] correction radiométrique [Termes IGN] filtrage numérique d'image [Termes IGN] image multibande [Termes IGN] précision de la classification [Termes IGN] semis de points [Termes IGN] télémétrie laser terrestre Résumé : (Auteur) A method is presented for filtering and classification of terrestrial laser scanner point clouds. The algorithm exploits the four-channel (blue, green, red and near infrared) multispectral imaging capability of some terrestrial scanners using supervised, parametric classification to assign thematic class labels to all scan cloud points. Its principal advantage is that it is a completely data-driven algorithm and is independent of spatial sampling resolution since the processing is performed in four-dimensional spectral feature space. Its application to two data-sets of different spatial extent and spatial and spectral complexity is reported, for which respective overall classification accuracies of 87.0% and 82.0% were achieved. Analysis of the input data with emphasis on the characteristics pertinent to the anticipated outcomes precedes detailed analysis of the classification results and error sources and their causes. Erroneously classified points are attributed to radiometric errors stemming from both detector hardware and physical effects. Numéro de notice : A2005-378 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2005.00321.x En ligne : https://doi.org/10.1111/j.1477-9730.2005.00321.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27514 [article]

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A quantitative comparison of methods for classifying burned areas with LISS-3 imagery / R.M. Roman-Cuesta in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 9 (May 2005) . - pp 1979 - 2003 Titre : A quantitative comparison of methods for classifying burned areas with LISS-3 imagery Type de document : Article/Communication Auteurs : R.M. Roman-Cuesta, Auteur ; J. Retana, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 1979 - 2003 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] analyse multibande [Termes IGN] classificateur paramétrique [Termes IGN] classification dirigée [Termes IGN] image IRS-LISS [Termes IGN] impact sur l'environnement [Termes IGN] incendie de forêt [Termes IGN] surveillance écologique Résumé : (Auteur) Environmental agencies frequently require tools for quick assessments of areas affected by large fires. Remote sensing techniques have been reported as efficient tools to evaluate the effects of fire. However, there exist few quantitative comparisons about the performance of the diverse methods. This study quantitatively evaluated the accuracy of five different techniques, a field survey and four satellite-based techniques, in order to quickly classify a large forest fire that occurred in 1998 in Solsonès (north-east Spain) by means of an IRS LISS-3 image. Three pure classes were determined: burned area, unburned vegetation, and bare soil; along with a non-pure class that we called mixed area. These selected techniques were included into a tree classifier to investigate their partial contribution to the final classification. The most accurate methods when focusing on pure classes were those directly related to the spectral characteristics of the pixel: Reflectance Data and Spectral Unmixing (82% of overall accuracy), versus the poorer performances of Vegetation Indices (70%), Textural measures (72%) and the field survey (68.6%). Since no image processing technique was applied to the Raw Reflectance Data, it can be considered the most cost-effective method, and the tree classifier reinforces its importance. The results of this study reveal that time consuming and expensive methods are not necessarily the most accurate, especially when potentially easily distinguishable classes are involved. Numéro de notice : A2005-258 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331299315 En ligne : https://doi.org/10.1080/01431160512331299315 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27394 [article]

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Radial basis function neural networks classification using very high spatial resolution satellite imagery: an application to the habitat area of Lake Kerkini (Greece) / Iphigenia Keramitsoglou in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 26 n° 9 (May 2005) . - pp 1861 - 1880 Titre : Radial basis function neural networks classification using very high spatial resolution satellite imagery: an application to the habitat area of Lake Kerkini (Greece) Type de document : Article/Communication Auteurs : Iphigenia Keramitsoglou, Auteur ; H. Sarimveis, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 1861 - 1880 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse texturale [Termes IGN] bande spectrale [Termes IGN] classificateur paramétrique [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal [Termes IGN] fonction de base radiale [Termes IGN] Grèce [Termes IGN] image à très haute résolution [Termes IGN] lac Résumé : (Auteur) This study investigates the potential of applying the radial basis function (RBF) neural network architecture for the classification of multispectral very high spatial resolution satellite images into 13 classes of various scales. For the development of the RBF classifiers, the innovative fuzzy means training algorithm is utilized, which is based on a fuzzy partition of the input space. The method requires only a short amount of time to select both the structure and the parameters of the RBF classifier. The new technique was applied to the area of Lake Kerkini, which is a wetland of great ecological value, located in northern Greece. Eleven experiments were carried out in total in order to investigate the performance of the classifier using different input parameters (spectral and textural) as well as different window sizes and neural network complexities. For comparison purposes the same satellite scene was classified using the maximum likelihood (MLH) classification with the same set of training samples. Overall, the neural network classifiers outperformed the MLH classification by 10-17%, reaching a maximum overall accuracy of 78%. Analysis showed that the selection of input parameters is vital for the success of the classifiers. On the other hand, the incorporation of textural analysis and/or modification of the window size do not affect the performance substantially. Numéro de notice : A2005-255 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160512331326594 En ligne : https://doi.org/10.1080/01431160512331326594 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27391 [article]

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A method for detecting large-scale forest covers change using coarse spatial resolution imagery / R.H. Fraser in Remote sensing of environment, vol 95 n° 4 (30/04/2005)  

Public [article]  inRemote sensing of environment > vol 95 n° 4 (30/04/2005) . - pp 414 - 427 Titre : A method for detecting large-scale forest covers change using coarse spatial resolution imagery Type de document : Article/Communication Auteurs : R.H. Fraser, Auteur ; A. Abuelgasim, Auteur ; R. Latifovic, Auteur Année de publication : 2005 Article en page(s) : pp 414 - 427 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Canada [Termes IGN] classificateur paramétrique [Termes IGN] classification par arbre de décision [Termes IGN] couvert forestier [Termes IGN] détection de changement [Termes IGN] données auxiliaires [Termes IGN] grande échelle [Termes IGN] image à basse résolution [Termes IGN] image à moyenne résolution [Termes IGN] image NOAA-AVHRR [Termes IGN] image SPOT-Végétation [Termes IGN] modèle de régression [Termes IGN] régression [Termes IGN] surveillance forestière Résumé : (Auteur) Many large countries, including Canada, rely on earth observation as a practical and cost-effective means of monitoring their vast inland ecosystems. A potentially efficient approach is one that detects vegetation changes over a hierarchy of spatial scales ranging from coarse to fine. This paper presents a Change Screening Analysis Technique (Change-SAT) designed as a coarse filter to identify the location and timing of large (>5-1 0 kM2) forest cover changes caused by anthropogenic and natural disturbances at an annual, continental scale. The method uses change metrics derived from 1-km multi-temporal SPOT VEGETATION and NOAA AVHRR imagery (reflectance, temperature, and texture information) and ancillary spatial variables (proximity to active fires, roads, and forest tenures) in combination with logistic regression and decision tree classifiers. Major forest changes of interest include wildfires, insect defoliation, forest harvesting and flooding. Change-SAT was tested for 1998-2000 using an independent sample of change and no-change sites over Canada. Overall accuracy was 94% and commission error, especially critical for large-area change applications, was less than 1%. Regions identified as having major or widespread changes could be targeted for more detailed investigation and mapping using field visits, aerial survey or fine resolution EO methods, such as those being applied under Canadian monitoring programs. This multi-resolution approach could be used as pan of a forest monitoring system to report on carbon stocks and forest stewardship. Numéro de notice : A2005-186 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.12.014 En ligne : https://doi.org/10.1016/j.rse.2004.12.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27323 [article]

Signature extension through space for northern landcover classification: a comparison of radiometric correction methods / I. Olthof in Remote sensing of environment, vol 95 n° 3 (15/04/2005)  

Public [article]  inRemote sensing of environment > vol 95 n° 3 (15/04/2005) . - pp 290 - 302 Titre : Signature extension through space for northern landcover classification: a comparison of radiometric correction methods Type de document : Article/Communication Auteurs : I. Olthof, Auteur ; C. Butson, Auteur ; R. Fraser, Auteur Année de publication : 2005 Article en page(s) : pp 290 - 302 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] agriculture [Termes IGN] analyse comparative [Termes IGN] classificateur paramétrique [Termes IGN] correction radiométrique [Termes IGN] image Landsat [Termes IGN] limite de résolution géométrique [Termes IGN] occupation du sol [Termes IGN] phénologie [Termes IGN] prévision [Termes IGN] signature spectrale Résumé : (Auteur) Northern landcover mapping for climate change and carbon modeling requires greater detail than what is available from coarse resolution data. Mapping landcover with medium resolution data from Landsat presents challenges due to differences in time and space between scene acquisitions required for full coverage. These differences cause landcover signatures to vary due to haze, solar geometry and phenology, among other factors. One way to circumvent this problem is to have an image interpreter classify each scene independently, however, this is not an optimal solution in the north due to a lack of spatially extensive reference data and resources required to label scenes individually. Another possible approach is to stabilize signatures in space and time so that they may be extracted from one scene and extended to others, thereby reducing the amount of reference data and user input required for mapping large areas. A radiometric normalization approach was developed that exploits the high temporal frequency with which coarse resolution data are acquired and the high spatial frequency of medium resolution data. The current paper compares this radiometric correction methodology with an established absolute calibration methodology for signature extension for landcover classification and explores factors that affect extension performance to recommend how and when signature extension can be applied. Overall, the new normalization method produced better extension and classification results than absolute calibration. Results also showed that extension performance was affected more by geographical distance than by differences in anniversary dates between acquisitions for the range of data examined. Geographical distance in the north-south direction leads to poorer extension performance than distance in the cast west direction due in part to differences in vegetation composition assigned the same class label in the latitudinal direction. While extension performance was somewhat variable and in some cases did not produce a best classification result by itself, it provided an initial best guess of landcover that can subsequently be refined by an expert image interpreter. Numéro de notice : A2005-170 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.12.015 En ligne : https://doi.org/10.1016/j.rse.2004.12.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27308 [article]

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)  

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Extraction semi-automatique de bâtiments à partir d'images satellitaires / Z. Mabed (2005)

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Knowledge-based approaches to accurate mapping of mangroves from satellite data / J. Gao in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 11 (November 2004)  

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Classifying land development in high-resolution panchromatic satellite images using straight-line statistics / C. Unsalan in IEEE Transactions on geoscience and remote sensing, vol 42 n° 4 (April 2004)  

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Carbon mass fluxes of forests in Belgium determined with low resolution optical sensors / F. Veroustraete in International Journal of Remote Sensing IJRS, vol 25 n° 4 (February 2004)  

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Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data / N. Oppelt in International Journal of Remote Sensing IJRS, vol 25 n° 1 (January 2004)  

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A Markov random field approach to spatio-temporal contextual image classification / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 41 n° 11 (November 2003)  

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Error tracking in Ikonos geometric processing using a 3D parametric model / Thierry Toutin in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 1 (January 2003)

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The UK land cover map 2000: construction of a parcel-based vector map from satellite images / R.M. Fuller in Cartographic journal (the), vol 39 n° 1 (June 2002)  

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