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Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping / L. Drăguț in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 7- 8 (July - August 2016) . - pp 1359 - 1376 Titre : Land-surface segmentation as a method to create strata for spatial sampling and its potential for digital soil mapping Type de document : Article/Communication Auteurs : L. Drăguț, Auteur ; A. Dornik, Auteur Année de publication : 2016 Article en page(s) : pp 1359 - 1376 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse d'image numérique [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse de groupement [Termes IGN] cartographie numérique [Termes IGN] échantillonnage de données [Termes IGN] modèle numérique de surface [Termes IGN] validation des données Résumé : (Auteur) Sampling efforts are constrained by limited availability of resources. Therefore, methods to reduce the number of samples, while still achieving reasonable accuracy are needed. Land-surface segmentation (LSS) has proven a powerful technique to partition digital elevation models (DEMs) and their derivatives into relatively homogeneous areas, which can be further employed as support in soil sampling. Though topography is one of the main soil forming factors, a robust assessment of the potential of this technique to digital soil mapping (DSM) is still missing. In this study, we aimed at evaluating the potential of LSS in stratifying a landscape into relatively homogeneous areas, which can be used as strata for guiding the selection of sampling points in DSM. The experiments were carried out in two study areas where soil samples were available. Land-surface derivatives were derived from DEMs and segmented with a tool based on the multiresolution segmentation algorithm, into objects considered as homogeneous soil-landscape divisions. Thus, one sample was randomly selected within each segment from the existing sample data, based on which predictions of soil classes/sub-orders and properties, i.e. soil texture and A-horizon thickness, were made. Results were compared with predictions based on simple random sampling (SRS) and conditioned Latin hypercube (cLHS). The segmentation-based sampling (SBS) scheme performed better than SRS and cLHS schemes in predicting the A-horizon thickness, soil texture fractions and soil classes, showing a high potential of LSS in stratifying a landscape for the purposes of DSM. The novelty of this study is in the way strata are constructed, rather than in the sampling design itself. Further research is needed to demonstrate the value of a SBS design for practical use. The analyses presented here further highlight the importance of considering locally adaptive techniques in optimization of sampling schemes and predictions of soil properties. Numéro de notice : A2016-307 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1131828 En ligne : http://dx.doi.org/10.1080/13658816.2015.1131828 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80906 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2016042	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2016041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Automated parameterisation for multi-scale image segmentation on multiple layers / L. Drăguț 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 119 - 127 Titre : Automated parameterisation for multi-scale image segmentation on multiple layers Type de document : Article/Communication Auteurs : L. Drăguț, Auteur ; O. Csillik, Auteur ; C. Eisank, Auteur ; D. Tiede, Auteur Année de publication : 2014 Article en page(s) : pp 119 - 127 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] eCognition [Termes IGN] facteur d'échelle [Termes IGN] résolution multiple [Termes IGN] segmentation d'image [Termes IGN] variance Résumé : (Auteur) We introduce a new automated approach to parameterising multi-scale image segmentation of multiple layers, and we implemented it as a generic tool for the eCognition® software. This approach relies on the potential of the local variance (LV) to detect scale transitions in geospatial data. The tool detects the number of layers added to a project and segments them iteratively with a multi-resolution segmentation algorithm in a bottom-up approach, where the scale factor in the segmentation, namely, the scale parameter (SP), increases with a constant increment. The average LV value of the objects in all of the layers is computed and serves as a condition for stopping the iterations: when a scale level records an LV value that is equal to or lower than the previous value, the iteration ends, and the objects segmented in the previous level are retained. Three orders of magnitude of SP lags produce a corresponding number of scale levels. Tests on very high resolution imagery provided satisfactory results for generic applicability. The tool has a significant potential for enabling objectivity and automation of GEOBIA analysis. Numéro de notice : A2014-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.018 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32993 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014021	RAB	Revue	Centre de documentation	En réserve L003	Disponible