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Reducing mis-registration and shadow effects on change detection in wetlands / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 4 (April 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 4 (April 2011) . - pp 325 - 334 Titre : Reducing mis-registration and shadow effects on change detection in wetlands Type de document : Article/Communication Auteurs : Jinxia Zhu, Auteur ; Q. Guo, Auteur ; D. Li, Auteur ; T. Harmon, Auteur Année de publication : 2011 Article en page(s) : pp 325 - 334 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] axe de prise de vue [Termes IGN] classification orientée objet [Termes IGN] correction des ombres [Termes IGN] détection de changement [Termes IGN] marais [Termes IGN] ombre [Termes IGN] seuillage d'image [Termes IGN] superposition d'images Résumé : (Auteur) With respect to the inevitable mis-registration and shadow effects on change detection analysis, we propose object-based post-classification of the Multivariate Alteration Detection components (ob-mad). Very high spatial resolution images of drained, managed wetland ponds were used to compare the proposed OB-MAD method with three commonly used classification methods in terms of minimizing the influence of mis-registration and shadow on the change detection analysis: (a) the traditional mad method with thresholds (Threshold-MAD), (b) a pixel-based post-classification of mad components with decision tree analysis (PB-MAD), and (c) a traditional object-based post-classification method (OB-traditional). The OB-MAD method, which utilizes shape and textural information of objects derived from MAD components, produced the highest accuracy with respect to wetland change detection and successfully minimized the influence from the geometric distortion and shadow on the changed area. Numéro de notice : A2011-127 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.4.325 En ligne : https://doi.org/10.14358/PERS.77.4.325 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30906 [article]

A genetic programming approach to estimate vegetation cover in the context of soil erosion assessment / C. Puente in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 4 (April 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 4 (April 2011) . - pp 363 - 376 Titre : A genetic programming approach to estimate vegetation cover in the context of soil erosion assessment Type de document : Article/Communication Auteurs : C. Puente, Auteur ; G. Olague, Auteur ; S. Smith, Auteur ; et al., Auteur Année de publication : 2011 Article en page(s) : pp 363 - 376 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme génétique [Termes IGN] couvert végétal [Termes IGN] érosion [Termes IGN] indice de végétation [Termes IGN] modèle RUSLE [Termes IGN] occupation du sol [Termes IGN] Soil Adjusted Vegetation Index Résumé : (Auteur) This work describes a genetic programming (GP) approach that creates vegetation indices (vi's) to automatically detect the sum of healthy, dry, and dead vegetation. Nowadays, it is acknowledged that VI's are the most popular method for extracting vegetation information from satellite imagery. In particular, erosion models like the "Revised Universal Soil Loss Equation" (RUSLE) can use VI's as input to measure the effects of the RUSLE soil cover factor (C). However, the results are generally incomplete, because most indices recognize only healthy vegetation. The aim of this study is to devise a novel approach for designing new VI's that are better correlated with C, using field and satellite information. Our approach consists on stating the problem in terms of optimization through GP learning, building novel indices by iteratively recombining a set of numerical operators and spectral channels until the best composite operator is found. Experimental results illustrate the efficiency and reliability of our approach in contrast with traditional indices like those of the NDVI and SAVI family. This study provides evidence that similar problems related to soil erosion assessment could be analyzed with our proposed methodology. Numéro de notice : A2011-128 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.4.363 En ligne : https://doi.org/10.14358/PERS.77.4.363 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30907 [article]

Historical land use as a feature for image classification / Jorge Abel Recio in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 4 (April 2011)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 4 (April 2011) . - pp 377 - 387 Titre : Historical land use as a feature for image classification Type de document : Article/Communication Auteurs : Jorge Abel Recio, Auteur ; Txomin Hermosilla, Auteur ; L. Ruiz, Auteur ; A. Fernandez-Sarria, Auteur Année de publication : 2011 Article en page(s) : pp 377 - 387 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] changement d'occupation du sol [Termes IGN] classification dirigée [Termes IGN] détection de changement [Termes IGN] données spatiotemporelles [Termes IGN] mise à jour de base de données [Termes IGN] précision de la classification [Termes IGN] utilisation du sol Résumé : (Auteur) This paper analyzes the effect of the addition of historical land-use as a descriptive feature in plot-based image classification when updating land-use/land-cover geospatial databases. Several historical databases have been simulated to assess the influence and significance of this feature in the classification. The causes, nature, and evolution of classification errors as the database currency varies are analyzed; and the impact of these errors on change detection during the updating process is evaluated. The results show that the addition of historical land-use information increases the overall accuracy of image classifications. During a database updating process, changes are detected by comparing the historical land-use with the classification results. The main drawback of employing historical land-use as a descriptive feature in image classification for change detection is that the percentage of undetectable errors significantly increases as more accurate is the database information. Numéro de notice : A2011-129 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.4.377 En ligne : https://doi.org/10.14358/PERS.77.4.377 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30908 [article]