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Histogram curve matching approaches for object-based image classification of land cover and land use / Sory I. Toure in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 5 (May 2013)
[article]
Titre : Histogram curve matching approaches for object-based image classification of land cover and land use Type de document : Article/Communication Auteurs : Sory I. Toure, Auteur ; Douglas A. Stow, Auteur ; John R. Weeks, Auteur ; Sunil Kumar, Auteur Année de publication : 2013 Article en page(s) : pp 433 - 440 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] appariement d'histogramme
[Termes IGN] classificateur
[Termes IGN] classification barycentrique
[Termes IGN] classification orientée objet
[Termes IGN] image multibande
[Termes IGN] occupation du sol
[Termes IGN] San DiegoRésumé : (Auteur) The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploit-ing characteristic signatures of such histograms. Two histo-grams matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5m, 2.5m, and 5m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently per-formed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5m spatial resolution. Numéro de notice : A2013-281 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.5.433 En ligne : https://doi.org/10.14358/PERS.79.5.433 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32419
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 5 (May 2013) . - pp 433 - 440[article]Sensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area / Magdalini Pleniou in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)
[article]
Titre : Sensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area Type de document : Article/Communication Auteurs : Magdalini Pleniou, Auteur ; Nikos Koustias, Auteur Année de publication : 2013 Article en page(s) : pp 199 - 210 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] affinage d'image
[Termes IGN] analyse comparative
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] Grèce
[Termes IGN] image Ikonos
[Termes IGN] image Landsat-SWIR
[Termes IGN] image Terra-ASTER
[Termes IGN] incendie de forêt
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] régression multiple
[Termes IGN] sol nuRésumé : (Auteur) The aim of our study was to explore the spectral properties of fire-scorched (burned) and non fire-scorched (vegetation) areas, as well as areas with different burn/vegetation ratios, using a multisource multiresolution satellite data set. A case study was undertaken following a very destructive wildfire that occurred in Parnitha, Greece, July 2007, for which we acquired satellite images from LANDSAT, ASTER, and IKONOS. Additionally, we created spatially degraded satellite data over a range of coarser resolutions using resampling techniques. The panchromatic (1 m) and multispectral component (4 m) of IKONOS were merged using the Gram-Schmidt spectral sharpening method. This very high-resolution imagery served as the basis to estimate the cover percentage of burned areas, bare land and vegetation at pixel level, by applying the maximum likelihood classification algorithm. Finally, multiple linear regression models were fit to estimate each land-cover fraction as a function of surface reflectance values of the original and the spatially degraded satellite images. The main findings of our research were: (a) the Near Infrared (NIR) and Short-wave Infrared (SWIR) are the most important channels to estimate the percentage of burned area, whereas the NIR and red channels are the most important to estimate the percentage of vegetation in fire-affected areas; (b) when the bi-spectral space consists only of NIR and SWIR, then the NIR ground reflectance value plays a more significant role in estimating the percent of burned areas, and the SWIR appears to be more important in estimating the percent of vegetation; and (c) semi-burned areas comprising 45–55% burned area and 45–55% vegetation are spectrally closer to burned areas in the NIR channel, whereas those areas are spectrally closer to vegetation in the SWIR channel. These findings, at least partially, are attributed to the fact that: (i) completely burned pixels present low variance in the NIR and high variance in the SWIR, whereas the opposite is observed in completely vegetated areas where higher variance is observed in the NIR and lower variance in the SWIR, and (ii) bare land modifies the spectral signal of burned areas more than the spectral signal of vegetated areas in the NIR, while the opposite is observed in SWIR region of the spectrum where the bare land modifies the spectral signal of vegetation more than the burned areas because the bare land and the vegetation are spectrally more similar in the NIR, and the bare land and burned areas are spectrally more similar in the SWIR. Numéro de notice : A2013-237 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.02.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.02.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32375
in ISPRS Journal of photogrammetry and remote sensing > vol 79 (May 2013) . - pp 199 - 210[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013051 RAB Revue Centre de documentation En réserve L003 Disponible Assessing reference dataset representativeness through confidence metrics based on information density / Giorgos Mountrakis in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)
[article]
Titre : Assessing reference dataset representativeness through confidence metrics based on information density Type de document : Article/Communication Auteurs : Giorgos Mountrakis, Auteur ; Bo Xi, Auteur Année de publication : 2013 Article en page(s) : pp 129 - 147 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de sensibilité
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte de confiance
[Termes IGN] classification dirigée
[Termes IGN] densité d'information
[Termes IGN] données localisées de référence
[Termes IGN] jeu de données localisées
[Termes IGN] representativitéRésumé : (Auteur) Land cover maps obtained from classification of remotely sensed imagery provide valuable information in numerous environmental monitoring and modeling tasks. However, many uncertainties and errors can directly or indirectly affect the quality of derived maps. This work focuses on one key aspect of the supervised classification process of remotely sensed imagery: the quality of the reference dataset used to develop a classifier. More specifically, the representative power of the reference dataset is assessed by contrasting it with the full dataset (e.g. entire image) needing classification. Our method is applicable in several ways: training or testing datasets (extracted from the reference dataset) can be compared with the full dataset. The proposed method moves beyond spatial sampling schemes (e.g. grid, cluster) and operates in the multidimensional feature space (e.g. spectral bands) and uses spatial statistics to compare information density of data to be classified with data used in the reference process. The working hypothesis is that higher information density, not in general but with respect to the entire classified image, expresses higher confidence in obtained results. Presented experiments establish a close link between confidence metrics and classification accuracy for a variety of image classifiers namely maximum likelihood, decision tree, Backpropagation Neural Network and Support Vector Machine. A sensitivity analysis demonstrates that spatially-continuous reference datasets (e.g. a square window) have the potential to provide similar classification confidence as typically-used spatially-random datasets. This is an important finding considering the higher acquisition costs for randomly distributed datasets. Furthermore, the method produces confidence maps that allow spatially-explicit comparison of confidence metrics within a given image for identification of over- and under-represented image portions. The current method is presented for individual image classification but, with sufficient evaluation from the remote sensing community it has the potential to become a standard for reference dataset reporting and thus allowing users to assess representativeness of reference datasets in a consistent manner across different classification tasks. Numéro de notice : A2013-183 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.01.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.01.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32321
in ISPRS Journal of photogrammetry and remote sensing > vol 78 (April 2013) . - pp 129 - 147[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013041 RAB Revue Centre de documentation En réserve L003 Disponible Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy / I.D. Sanches in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)
[article]
Titre : Assessing the impact of hydrocarbon leakages on vegetation using reflectance spectroscopy Type de document : Article/Communication Auteurs : I.D. Sanches, Auteur ; C.R. Souza Filho, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 85 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse en composantes principales
[Termes IGN] canalisation
[Termes IGN] canopée
[Termes IGN] détection de changement
[Termes IGN] feuille (végétation)
[Termes IGN] forêt tropicale
[Termes IGN] hydrocarbure
[Termes IGN] réflectance végétaleRésumé : (Auteur) This paper assesses the capability of hyperspectral remote sensing to detect hydrocarbon leakages in pipelines using vegetation status as an indicator of contamination. A field experiment in real scale and in tropical weather was conducted in which Brachiaria brizantha H.S. pasture plants were grown over soils contaminated with small volumes of liquid hydrocarbons (HCs). The contaminations involved volumes of hydrocarbons that ranged between 2 L and 12.7 L of gasoline and diesel per m3 of soil, which were applied to the crop parcels over the course of 30 days. The leaf and canopy reflectance spectra of contaminated and control plants were acquired within 350–2500 nm wavelengths. The leaf and canopy reflectance spectra were mathematically transformed by means of first derivative (FD) and continuum removal (CR) techniques. Using principal component analysis (PCA), the spectral measurements could be grouped into either two or three contamination groups. Wavelengths in the red edge were found to contain the largest spectral differences between plants at distinct, evolving contamination stages. Wavelengths centred on water absorption bands were also important to differentiating contaminated from healthy plants. The red edge position of contaminated plants, calculated on the basis of FD spectra, shifted substantially to shorter wavelengths with increasing contamination, whereas non-contaminated plants displayed a red shift (in leaf spectra) or small blue shift (in canopy spectra). At leaf scale, contaminated plants were differentiated from healthy plants between 550–750 nm, 1380–1550 nm, 1850–2000 nm and 2006–2196 nm. At canopy scale, differences were substantial between 470–518 nm, 550–750 nm, 910–1081 nm, 1116–1284 nm, 1736–1786 nm, 2006–2196 nm and 2222–2378 nm. The results of this study suggests that remote sensing of B. brizantha H.S. at both leaf and canopy scales can be used as an indicator of gasoline and diesel contaminations for the detection of small leakages in pipelines. Numéro de notice : A2013-181 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.01.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.01.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32319
in ISPRS Journal of photogrammetry and remote sensing > vol 78 (April 2013) . - pp 85 - 101[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013041 RAB Revue Centre de documentation En réserve L003 Disponible Fast error analysis of continuous GNSS observations with missing data / M.S. Bos in Journal of geodesy, vol 87 n° 4 (April 2013)
[article]
Titre : Fast error analysis of continuous GNSS observations with missing data Type de document : Article/Communication Auteurs : M.S. Bos, Auteur ; R. Fernandes, Auteur ; S. Williams, Auteur ; Luisa Bastos, Auteur Année de publication : 2013 Article en page(s) : pp 351 - 360 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] bruit (théorie du signal)
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données GNSS
[Termes IGN] matrice de covariance
[Termes IGN] série temporelle
[Termes IGN] traitement de données GNSSRésumé : (Auteur) One of the most widely used method for the time-series analysis of continuous Global Navigation Satellite System (GNSS) observations is Maximum Likelihood Estimation (MLE) which in most implementations requires O(n3) operations for n observations. Previous research by the authors has shown that this amount of operations can be reduced to O(n2) for observations without missing data. In the current research we present a reformulation of the equations that preserves this low amount of operations, even in the common situation of having some missing data. Our reformulation assumes that the noise is stationary to ensure a Toeplitz covariance matrix. However, most GNSS time-series exhibit power-law noise which is weakly non-stationary. To overcome this problem, we present a Toeplitz covariance matrix that provides an approximation for power-law noise that is accurate for most GNSS time-series. Numerical results are given for a set of synthetic data and a set of International GNSS Service (IGS) stations, demonstrating a reduction in computation time of a factor of 10–100 compared to the standard MLE method, depending on the length of the time-series and the amount of missing data. Numéro de notice : A2013-218 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-012-0605-0 Date de publication en ligne : 02/12/2012 En ligne : https://doi.org/10.1007/s00190-012-0605-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32356
in Journal of geodesy > vol 87 n° 4 (April 2013) . - pp 351 - 360[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 266-2013041 SL Revue Centre de documentation Revues en salle Disponible Footprint generation using fuzzy-neighborhood clustering / Jonathon K. 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Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)PermalinkA framework for the registration and segmentation of heterogeneous lidar data / M. Al-Durgham in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 2 (February 2013)PermalinkA graph-based classification method for hyperspectral images / J. Bai in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)PermalinkGround filtering and vegetation mapping using multi-return terrestrial laser scanning / Francesco Pirotti in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)PermalinkImplementation of the 1:10 000 scale for visualisation of environmental changes / Radzym Lawniczack in Cartographic journal (the), vol 50 n° 1 (February 2013)PermalinkModel driven reconstruction of roofs from sparse LIDAR point clouds / A. 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