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An assessment of geometric activity features for per-pixel classification of urban man-made objects using Very High Resolution satellite Imagery / J. Chan in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 4 (April 2009) . - pp 397 - 411 Titre : An assessment of geometric activity features for per-pixel classification of urban man-made objects using Very High Resolution satellite Imagery Type de document : Article/Communication Auteurs : J. Chan, Auteur ; R. Bellens, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 397 - 411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification pixellaire [Termes IGN] détail topographique artificiel [Termes IGN] eCognition [Termes IGN] image à très haute résolution [Termes IGN] milieu urbain [Termes IGN] objet géographique urbain [Termes IGN] précision de la classification [Termes IGN] traitement géométrique de données Résumé : (Auteur) In this paper, we propose the use of Geometric Activity (GA) features for detecting man-made objects in urban areas using VHR satellite imagery. These features describe the geometric context of a pixel without the necessity of segmentation and can be integrated as extra bands in a per-pixel classification. Two main types of GA features were investigated: ridge features based on the well-known facet model and morphological features obtained by applying closing transforms with structuring elements of different size and shape. Our findings show a substantial increase in classification accuracy for the man-made object classes “roads and buildings with dark roof” after inclusion of GA features. Next to GA features, the use of object-based features derived from eCognition®, containing both geometric and textural information, was also investigated for per-pixel classification. Accuracies obtained with object-based features are comparable to the accuracies obtained with GA features. The inclusion of both GA features and object-based features further improves the overall accuracy. GA features and object-based features thus contain complementary information. Copyright ASPRS Numéro de notice : A2009-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.75.4.397 En ligne : https://doi.org/10.14358/PERS.75.4.397 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29736 [article]

Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast / C. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 4 (April 2009) . - pp 425 - 435 Titre : Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast Type de document : Article/Communication Auteurs : C. Yang, Auteur ; James H. Everitt, Auteur ; R.S. Fletcher, Auteur Année de publication : 2009 Article en page(s) : pp 425 - 435 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte de la végétation [Termes IGN] classification barycentrique [Termes IGN] classification par la distance de Mahalanobis [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification spectrale [Termes IGN] image aérienne [Termes IGN] image AISA+ [Termes IGN] image hyperspectrale [Termes IGN] Kappa de Cohen [Termes IGN] littoral [Termes IGN] mangrove [Termes IGN] Mexique (golfe du) Résumé : (Auteur) Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne AISA+ hyperspectral imagery and image transformation and classification techniques for mapping black mangrove populations on the south Texas Gulf coast. AISA+ hyperspectral imagery was acquired from two study sites and both minimum noise fraction (MNF) and inverse MNF transforms were performed. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the noise-reduced hyperspectral imagery and to the band-reduced MNF imagery for distinguishing black mangrove from associated plant species and other cover types. Accuracy assessment showed that overall accuracy varied from 84 percent to 95 percent for site 1 and from 69 percent to 91 percent for site 2 among the eight classifications for each site. The MNF images provided similar or better classification results compared with the hyperspectral images among the four classifiers. Kappa analysis showed that there were no significant differences among the four classifiers with the MNF imagery, though maximum likelihood provided excellent overall and class accuracies for both sites. Producer’s and user’s accuracies for black mangrove were 91 percent and 94 percent, respectively, for site 1 and both 91 percent for site 2 based on maximum likelihood applied to the MNF imagery. These results indicate that airborne hyperspectral imagery combined with image transformation and classification techniques can be a useful tool for monitoring and mapping black mangrove distributions in coastal environments. Copyright ASPRS Numéro de notice : A2009-107 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.75.4.425 En ligne : https://doi.org/10.14358/PERS.75.4.425 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29737 [article]

Morphology-based building detection from airborne Lidar data / X. Meng in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 4 (April 2009) . - pp 437 - 442 Titre : Morphology-based building detection from airborne Lidar data Type de document : Article/Communication Auteurs : X. Meng, Auteur ; L. Wang, Auteur ; N. Currit, Auteur Année de publication : 2009 Article en page(s) : pp 437 - 442 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Austin (Texas) [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] filtrage optique [Termes IGN] image multibande [Termes IGN] indice de végétation [Termes IGN] reconstruction 3D du bâti Résumé : (Auteur) The advent of Light Detection and Ranging (lidar) technique provides a promising resource for three-dimensional building detection. Due to the difficulty of removing vegetation, most building detection methods fuse lidar data with multispectral images for vegetation indices and relatively few approaches use only lidar data. However, the fusing process may cause errors introduced by resolution and time difference, shadow and high-rise building displacement problems, and the geo-referencing process. This research presents a morphological building detecting method to identify buildings by gradually removing non-building pixels. First, a ground-filtering algorithm separates ground pixels with buildings, trees, and other objects. Then, an analytical approach removes the remaining non-building pixels using size, shape, height, building element structure, and the height difference between the first and last returns. The experimental results show that this method provides a comparative performance with an overall accuracy of 95.46 percent as in a study site in Austin urban area. Copyright ASPRS Numéro de notice : A2009-108 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.75.4.437 En ligne : https://doi.org/10.14358/PERS.75.4.437 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29738 [article]