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A geometric method for wood-leaf separation using terrestrial and simulated Lidar data / Shengli Tao in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 10 (October 2015) . - pp 767 - 776 Titre : A geometric method for wood-leaf separation using terrestrial and simulated Lidar data Type de document : Article/Communication Auteurs : Shengli Tao, Auteur ; Qinghua Guo, Auteur ; Shiwu Xu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 767 - 776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] feuille (végétation) [Termes IGN] géométrie [Termes IGN] semis de points [Termes IGN] système de coordonnées [Termes IGN] traitement de données localisées [Termes IGN] tronc Résumé : (auteur) Terrestrial light detection and ranging (lidar) can be used to record the three-dimensional structures of trees. Wood-leaf separation, which aims to classify lidar points into wood and leaf components, is an essential prerequisite for deriving individual tree characteristics. Previous research has tended to use intensity (including a multi-wavelength approach) and waveform information for wood-leaf separation, but use of the most fundamental information from a lidar point cloud, i.e., the x-, y-, and z- coordinates of each point, for this purpose has been poorly explored. In this study, we introduce a geometric method for wood-leaf separation using the x-, y-, and zcoordinates of each point. The separation results indicate that first-, second-, and third-order branches can be extracted from the raw point cloud by this new method, suggesting that it might provide a promising solution for wood-leaf separation. Numéro de notice : A2015-987 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.81.10.767 En ligne : https://doi.org/10.14358/PERS.81.10.767 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80268 [article]

Two dimensional linear discriminant analyses for hyperspectral data / Maryam Imani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 10 (October 2015) . - pp 777 - 786 Titre : Two dimensional linear discriminant analyses for hyperspectral data Type de document : Article/Communication Auteurs : Maryam Imani, Auteur ; Hassan Ghassemian, Auteur Année de publication : 2015 Article en page(s) : pp 777 - 786 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] classification pixellaire [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] matrice Résumé : (auteur) Most supervised feature extraction methods like linear discriminant analysis (LDA) suffer from the limited number of available training samples. The singularity problem causes LDA to fail in small sample size (SSS) situations. Two dimensional linear discriminant analysis (2DLDA) for feature extraction of hyperspectral images is proposed in this paper which has good efficiency with small training sample size. In this approach, the feature vector of each pixel of hyperspectral image is transformed into a feature matrix. As a result, the data matrices lie in a low-dimensional space. Then, the between-class and within-class scatter matrices are calculated using the matrix form of training samples. The proposed approach has two main advantages: it deals with the SSS problem in hyperspectral data, and also it can extract each number of features (with no limitation) from the original high dimensional data. The proposed method is tested on four widely used hyperspectral datasets. Experimental results confirm that the proposed 2DLDA feature extraction method provides better classification accuracy, with a reasonable computation time, compared to popular supervised feature extraction methods such as generalized discriminant analysis (GDA) and nonparametric weighted feature extraction (NWFE) particularly compared to the 1DLDA in the SSS situation. The experiments show that two dimensional linear discriminant analysis + support vector machine (2DLDA+SVM) is an appropriate choice for feature extraction and classification of hyperspectral images using limited training samples. Numéro de notice : A2015-988 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.10.777 En ligne : https://doi.org/10.14358/PERS.81.10.777 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80269 [article]

Applying ASPRS accuracy standards to surveys from small unmanned aircraft systems (UAS) / Ken Whitehead in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 10 (October 2015) . - pp 787 - 793 Titre : Applying ASPRS accuracy standards to surveys from small unmanned aircraft systems (UAS) Type de document : Article/Communication Auteurs : Ken Whitehead, Auteur ; Chris H. Hugenholtz, Auteur Année de publication : 2015 Article en page(s) : pp 787 - 793 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] drone [Termes IGN] modèle numérique de surface [Termes IGN] norme de données localisées [Termes IGN] orthoimage [Termes IGN] point d'appui [Termes IGN] précision des données Résumé : (auteur) We present a first assessment of UAS-derived orthoimagery and digital elevation data in the context of newly-released accuracy standards for digital geospatial data developed by the American Society for Photogrammetry and Remote Sensing. We outline results from two case studies using a commercially-available UAS, photogrammetry software, and an array of ground control and check points. Radial horizontal and vertical root-meansquare- errors (RMSE) were calculated as 0.05 m and 0.06 m, respectively, for one site, and 0.08 m and 0.03 m, respectively, for the other. Under the 1990 ASPRS standards, both surveys meet the requirements for Class 1 accuracy at the 1:500 map scale and at the 0.50 m contour interval. Under the newly-developed ASPRS standards, the reported errors fulfill the requirements for both horizontal and vertical mapping at the 10 cm RMSE level. Overall, these results provide initial direction for practitioners considering UAS surveying in the context of accuracy standards Numéro de notice : A2015-989 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.10.787 En ligne : https://doi.org/10.14358/PERS.81.10.787 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80270 [article]