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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]

Approaches to fractional land cover and continuous field mapping: a comparative assessment over the BOREAS [BOReal Ecosystem Atmosphere Study] study region / R. Fernandes in Remote sensing of environment, vol 89 n° 2 (30/01/2004)  

Public [article]  inRemote sensing of environment > vol 89 n° 2 (30/01/2004) . - pp 234 - 251 Titre : Approaches to fractional land cover and continuous field mapping: a comparative assessment over the BOREAS [BOReal Ecosystem Atmosphere Study] study region Type de document : Article/Communication Auteurs : R. Fernandes, Auteur ; R. Fraser, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 234 - 251 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] analyse de groupement [Termes IGN] carte d'occupation du sol [Termes IGN] classification par réseau neuronal [Termes IGN] image Landsat-TM [Termes IGN] image SPOT-Végétation [Termes IGN] inversion [Termes IGN] méthode des moindres carrés [Termes IGN] précision infrapixellaire [Termes IGN] régression multiple [Termes IGN] tâche image d'un point [Termes IGN] zone boréale Résumé : (Auteur) Subpixel land cover mapping involves the estimation of surface properties using sensors whose spatial sampling is coarse enough to produce mixtures of the properties within each pixel. This study evaluates five algorithms for mapping subpixel land cover fractions and continuous fields of vegetation properties within the BOREAS study area. The algorithms include a conventional "hard", perpixel classifier, a neural network, a clustering/look-up-table approach, multivariate regression, and linear least squares inversion. A land cover map prepared using a Landsat TM mosaic was adopted as the source of fine scale calibration and validation data. Coarse scale mixtures of five basic land cover classes and continuous vegetation fields, both corresponding to the field of view of SPOT-VEGETATION imagery (1.15-km pixel size), were synthesised from the TM mosaic using a modelled point spread function. Two measures of land cover distribution were used. fractions of fine scale land cover categories and continuous fields of vegetation structural characteristics. The subpixel algorithms were applied using both proximate ( 400 km) separation between training and validation regions. "Hard" classification performed poorly in estimating proportions or continuous fields. The neural network, look-up-table and multivariate regression algorithms produced good matches of spatial patterns and regional land cover composition for the proximate treatment. However, all three methods exhibited substantial biases with the distant treatment due to the characteristics of the training data. Linear least squares inversion offers a relatively unbiased but less precise alternative for subpixel proportion and fraction mapping as it avoids calibration to the a priori distribution of land cover in the training data. In general, a combination of multivariate regression for proximate training data and linear least squares inversion for distant training data resulted in woody fraction estimates within 20% of the Landsat TM classification-based estimates. Numéro de notice : A2004-026 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2002.06.006 En ligne : https://doi.org/10.1016/j.rse.2002.06.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26554 [article]