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Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching / Kasper Kansanen in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 152 (June 2019) . - pp 66 - 78 Titre : Estimating forest stand density and structure using Bayesian individual tree detection, stochastic geometry, and distribution matching Type de document : Article/Communication Auteurs : Kasper Kansanen, Auteur ; Jari Vauhkonen, Auteur ; Timo Lähivaara, Auteur ; Aku Seppänen, Auteur ; Matti Maltamo, Auteur ; Lauri Mehtätalo, Auteur Année de publication : 2019 Article en page(s) : pp 66 - 78 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] appariement d'histogramme [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] diamètre à hauteur de poitrine [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Finlande [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier local [Termes descripteurs IGN] Picea abies [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] placette d'échantillonnage [Termes descripteurs IGN] surface terrière [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Errors in individual tree detection and delineation affect diameter distribution predictions based on crown attributes extracted from the detected trees. We develop a methodology for circumventing these problems. The method is based on matching cumulative distribution functions of field measured tree diameter distributions and crown radii distributions extracted from airborne laser scanning data through individual tree detection presented by Vauhkonen and Mehtätalo (2015). In this study, empirical distribution functions and a monotonic, nonlinear model curve are introduced. Tree crown radius distribution produced by individual tree detection is corrected by a method taking into account that all trees cannot be detected. The evaluation is based on the ability of the developed model sequence to predict quadratic mean diameter and total basal area. The studied data consists of 36 field plots in a typical boreal managed forest area in eastern Finland. The suggested enhancements to the model sequence produce improved results in most of the test cases. Most notably, in leave-one-out cross-validation experiments the modified models improve RMSE of basal area 13% in the full data and RMSE of quadratic mean diameter and basal area 69% and 11%, respectively, in pure pine plots. Better modeling of the crown radius distribution and improved matching between crown radii and stem diameters add the operational premises of the full distribution matching. Numéro de notice : A2019-455 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.007 date de publication en ligne : 15/04/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92868 [article]

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Intersensor statistical matching for pansharpening : theoretical issues and practical solutions / Luciano Alparone in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4682 - 4695 Titre : Intersensor statistical matching for pansharpening : theoretical issues and practical solutions Type de document : Article/Communication Auteurs : Luciano Alparone, Auteur ; Andrea Garzelli, Auteur ; Gemine Vivone, Auteur Année de publication : 2017 Article en page(s) : pp 4682 - 4695 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] appariement d'histogramme [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] image Ikonos [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image multicapteur [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] résolution multiple Résumé : (Auteur) In this paper, the authors investigate the statistical matching of the panchromatic (Pan) image to the multispectral (MS) bands, also known as the histogram matching, for the two main classes of pansharpening methods, i.e., those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods. Also, hybrid methods combining CS with MRA, like the widespread additive wavelet luminance proportional (AWLP), are investigated. It is shown that all spectral, spatial, and hybrid methods must perform a dynamics matching of the enhancing Pan to the individual MS bands for MRA or a combination of them (the component that shall be substituted) for CS. For hybrid methods, the problem is more complex and both types of histogram matching may be suitable. Such an intersensor balance may be either explicit or implicitly performed by the detail-injection model, e.g., the popular projective and multiplicative injection models. An experimental setup exploiting IKONOS and WorldView-2 data sets demonstrates that a correct histogram matching is the key to attain extra performance from established methods. As a first result of this paper, the AWLP method has been revisited and its performance significantly improved by simply performing the histogram matching of Pan to the individual MS bands, rather than to the intensity component, thereby losing the original proportionality feature. Numéro de notice : A2017-502 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2697943 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2697943 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86447 [article]

A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation / Xiran Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 16 - 27 Titre : A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation Type de document : Article/Communication Auteurs : Xiran Zhou, Auteur ; Jun Liu, Auteur ; Lei Cao, Auteur ; Qiming Zhou, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 16 - 27 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de fusion [Termes descripteurs IGN] appariement d'histogramme [Termes descripteurs IGN] filtrage numérique d'image [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] modulation de fréquence [Termes descripteurs IGN] qualité d'image [Termes descripteurs IGN] transformation intensité-teinte-saturation Résumé : (Auteur) High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity–hue–saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS-based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods. Numéro de notice : A2014-081 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32986 [article]

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Remote sensing image segmentation by combining spectral and texture features / H. Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 1 tome 1 (January 2014) . - pp 16 - 16 Titre : Remote sensing image segmentation by combining spectral and texture features Type de document : Article/Communication Auteurs : H. Li, Auteur Année de publication : 2014 Article en page(s) : pp 16 - 16 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse combinatoire (maths) [Termes descripteurs IGN] appariement d'histogramme [Termes descripteurs IGN] image spectrale [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] texture d'image [Termes descripteurs IGN] valeur radiométrique Résumé : (Auteur) We present a new method for remote sensing image segmentation, which utilizes both spectral and texture information. Linear filters are used to provide enhanced spatial patterns. For each pixel location, we compute combined spectral and texture features using local spectral histograms, which concatenate local histograms of all input bands. We regard each feature as a linear combination of several representative features, each of which corresponds to a segment. Segmentation is given by estimating combination weights, which indicate segment ownership of pixels. We present segmentation solutions where representative features are either known or unknown. We also show that feature dimensions can be greatly reduced via subspace projection. The scale issue is investigated, and an algorithm is presented to automatically select proper scales, which does not require segmentation at multiple-scale levels. Experimental results demonstrate the promise of the proposed method. Numéro de notice : A2014-034 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32939 [article]

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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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 5 (May 2013) . - pp 433 - 440 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 descripteurs IGN] analyse comparative [Termes descripteurs IGN] appariement d'histogramme [Termes descripteurs IGN] classificateur [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] San Diego Résumé : (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 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 [article]

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Cloud-free satellite image mosaics with regression trees and histogram matching / E.H. Helmert in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 9 (September 2005)

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Topographic information of sand dunes as extracted from shading effects using Landsat images / N. Levin in Remote sensing of environment, vol 90 n° 2 (30/03/2004)

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True orthophoto generation of built-up areas using multi-view images / J.Y. Rau in Photogrammetric Engineering & Remote Sensing, PERS, vol 68 n° 6 (June 2002)

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Destriping multiple sensor imagery by improved histogram matching / M. Wegener in International Journal of Remote Sensing IJRS, vol 11 n° 5 (May 1990)

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