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Wavelet based image fusion techniques: an introduction, review and comparison / Krista Amolins in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 62 n° 4 (September 2007) . - pp 249 - 263 Titre : Wavelet based image fusion techniques: an introduction, review and comparison Type de document : Article/Communication Auteurs : Krista Amolins, Auteur ; Y. Zhang, Auteur ; Peter Dare, Auteur Année de publication : 2007 Article en page(s) : pp 249 - 263 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] déformation d'image [Termes IGN] distorsion d'image [Termes IGN] fusion d'images [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] transformation en ondelettes Résumé : (Auteur) Image fusion involves merging two or more images in such a way as to retain the most desirable characteristics of each. When a panchromatic image is fused with multispectral imagery, the desired result is an image with the spatial resolution and quality of the panchromatic imagery and the spectral resolution and quality of the multispectral imagery. Standard image fusion methods are often successful at injecting spatial detail into the multispectral imagery but distort the colour information in the process. Over the past decade, a significant amount of research has been conducted concerning the application of wavelet transforms in image fusion. In this paper, an introduction to wavelet transform theory and an overview of image fusion technique are given, and the results from a number of wavelet-based image fusion schemes are compared. It has been found that, in general, wavelet-based schemes perform better than standard schemes, particularly in terms of minimizing colour distortion. Schemes that combine standard methods with wavelet transforms produce superior results than either standard methods or simple wavelet-based methods alone. The results from wavelet-based methods can also be improved by applying more sophisticated models for injecting detail information; however, these schemes often have greater set-up requirements. Copyright ISPRS Numéro de notice : A2007-427 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2007.05.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2007.05.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28790 [article]

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Detection and discrimination between oil spills and look-alike phenomena through neural networks / Konstantinos Topouzelis in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 62 n° 4 (September 2007) . - pp 264 - 270 Titre : Detection and discrimination between oil spills and look-alike phenomena through neural networks Type de document : Article/Communication Auteurs : Konstantinos Topouzelis, Auteur ; V. Karathanassi, Auteur ; P. Pavlakis, Auteur ; D. Rokos, Auteur Année de publication : 2007 Article en page(s) : pp 264 - 270 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse discriminante [Termes IGN] classification par réseau neuronal [Termes IGN] détection automatique [Termes IGN] image radar moirée [Termes IGN] marée noire [Termes IGN] radargrammétrie Résumé : (Auteur) Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in the marine environment, as their recording is independent of clouds and weather. Dark formations can be caused by man made actions (e.g. oil spill discharging) or natural ocean phenomena (e.g. natural slicks, wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they damp the capillary and short gravity sea waves. The ability of neural networks to detect dark formations in high resolution SAR images and to discriminate oil spills from look-alike phenomena simultaneously was examined. Two different neural networks are used; one to detect dark formations and the second one to perform a classification to oil spills or look-alikes. The proposed method is very promising in detecting dark formations and discriminating oil spills from look-alikes as it detects with an overall accuracy of 94% the dark formations and discriminate correctly 89% of examined cases. Numéro de notice : A2007-428 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2007.05.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2007.05.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28791 [article]

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Filling the voids in the SRTM elevation model: a tin-based delta surface approach / E. Luedeling in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 62 n° 4 (September 2007) . - pp 283 - 294 Titre : Filling the voids in the SRTM elevation model: a tin-based delta surface approach Type de document : Article/Communication Auteurs : E. Luedeling, Auteur ; S. Siebert, Auteur ; A. Buerkert, Auteur Année de publication : 2007 Article en page(s) : pp 283 - 294 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] altitude [Termes IGN] analyse comparative [Termes IGN] erreur systématique [Termes IGN] fusion de données [Termes IGN] interpolation spatiale [Termes IGN] MNS SRTM [Termes IGN] modèle numérique de surface [Termes IGN] montagne [Termes IGN] Oman [Termes IGN] positionnement par GPS [Termes IGN] Triangulated Irregular Network Résumé : (Auteur) The Digital Elevation Model (DEM) derived from NASA's Shuttle Radar Topography Mission is the most accurate near-global elevation model that is publicly available. However, it contains many data voids, mostly in mountainous terrain. This problem is particularly severe in the rugged Oman Mountains. This study presents a method to fill these voids using a fill surface derived from Russian military maps. For this we developed a new method, which is based on Triangular Irregular Networks (TINs). For each void, we extracted points around the edge of the void from the SRTM DEM and the fill surface. TINs were calculated from these points and converted to a base surface for each dataset. The fill base surface was subtracted from the fill surface, and the result added to the SRTM base surface. The fill surface could then seamlessly be merged with the SRTM DEM. For validation, we compared the resulting DEM to the original SRTM surface, to the fill DEM and to a surface calculated by the International Center for Tropical Agriculture (CIAT) from the SRTM data. We calculated the differences between measured GPS positions and the respective surfaces for 187,500 points throughout the mountain range (?GPS). Comparison of the means and standard deviations of these values showed that for the void areas, the fill surface was most accurate, with a standard deviation of the ?GPS from the mean ?GPS of 69 m, and only little accuracy was lost by merging it to the SRTM surface (standard deviation of 76 m). The CIAT model was much less accurate in these areas (standard deviation of 128 m). The results show that our method is capable of transferring the relative vertical accuracy of a fill surface to the void areas in the SRTM model, without introducing uncertainties about the absolute elevation of the fill surface. It is well suited for datasets with varying altitude biases, which is a common problem of older topographic information. Copyright ISPRS Numéro de notice : A2007-429 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2007.05.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2007.05.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28792 [article]

Exemplaires(1)

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081-07061	SL	Revue	Centre de documentation	Revues en salle	Disponible

Estimation of vegetation parameter for modelling soil erosion using linear spectral mixture analysis of Landsat ETM data / A.M. DE Asis in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 62 n° 4 (September 2007) . - pp 309 - 324 Titre : Estimation of vegetation parameter for modelling soil erosion using linear spectral mixture analysis of Landsat ETM data Type de document : Article/Communication Auteurs : A.M. DE Asis, Auteur ; K. Omasa, Auteur Année de publication : 2007 Article en page(s) : pp 309 - 324 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] classification pixellaire [Termes IGN] couvert végétal [Termes IGN] données de terrain [Termes IGN] érosion [Termes IGN] estimation statistique [Termes IGN] image Landsat-ETM+ [Termes IGN] image Quickbird [Termes IGN] modèle physique [Termes IGN] modèle RUSLE Résumé : (Auteur) Soil conservation planning often requires estimates of soil erosion at a catchment or regional scale. Predictive models such as Universal Soil Loss Equation (USLE) and its subsequent Revised Universal Soil Loss Equation (RUSLE) are useful tools to generate the quantitative estimates necessary for designing sound conservation measures. However, large-scale soil erosion model-factor parameterization and quantification is difficult due to the costs, labor and time involved. Among the soil erosion parameters, the vegetative cover or C factor has been one of the most difficult to estimate over broad geographic areas. The C factor represents the effects of vegetation canopy and ground covers in reducing soil loss. Traditional methods for the extraction of vegetation information from remote sensing data such as classification techniques and vegetation indices were found to be inaccurate. Thus, this study presents a new approach based on Spectral Mixture Analysis (SMA) of Landsat ETM data to map the C factor for use in the modeling of soil erosion. A desirable feature of SMA is that it estimates the fractional abundance of ground cover and bare soils simultaneously, which is appropriate for soil erosion analysis. Hence, we estimated the C factor by utilizing the results of SMA on a pixel-by-pixel basis. We specifically used a linear SMA (LSMA) model and performed a minimum noise fraction (MNF) transformation and pixel purity index (PPI) on Landsat ETM image to derive the proportion of ground cover (vegetation and non-photosynthetic materials) and bare soil within a pixel. The end-members were selected based on the purest pixels found using PPI with reference to very high-resolution QuickBird image and actual field data. Results showed that the C factor value estimated using LSMA correlated strongly with the values measured in the field. The correlation coefficient (r) obtained was 0.94. A comparative analysis between NDVI- and LSMA-derived C factors also proved that the latter produced a more detailed spatial variability, as well as generated more accurate erosion estimates when used as input to RUSLE model. The QuickBird image coupled with field data was used in the validation of results. Copyright ISPRS Numéro de notice : A2007-430 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2007.05.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2007.05.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28793 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-07061	SL	Revue	Centre de documentation	Revues en salle	Disponible