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Auteur Qiang Wang |
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Development of a mixed pixel filter for improved dimension estimation using AMCW laser scanner / Qiang Wang in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)
[article]
Titre : Development of a mixed pixel filter for improved dimension estimation using AMCW laser scanner Type de document : Article/Communication Auteurs : Qiang Wang, Auteur ; Hoon Sohn, Auteur ; Jack C.P. Cheng, Auteur Année de publication : 2016 Article en page(s) : pp 246 - 258 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification pixellaire
[Termes IGN] données localisées 3D
[Termes IGN] filtre numérique
[Termes IGN] métrologie dimensionelle
[Termes IGN] précision des mesuresRésumé : (Auteur) Accurate dimension estimation is desired in many fields, but the traditional dimension estimation methods are time-consuming and labor-intensive. In the recent decades, 3D laser scanners have become popular for dimension estimation due to their high measurement speed and accuracy. Nonetheless, scan data obtained by amplitude-modulated continuous-wave (AMCW) laser scanners suffer from erroneous data called mixed pixels, which can influence the accuracy of dimension estimation. This study develops a mixed pixel filter for improved dimension estimation using AMCW laser scanners. The distance measurement of mixed pixels is firstly formulated based on the working principle of laser scanners. Then, a mixed pixel filter that can minimize the classification errors between valid points and mixed pixels is developed. Validation experiments were conducted to verify the formulation of the distance measurement of mixed pixels and to examine the performance of the proposed mixed pixel filter. Experimental results show that, for a specimen with dimensions of 840 mm × 300 mm, the overall errors of the dimensions estimated after applying the proposed filter are 1.9 mm and 1.0 mm for two different scanning resolutions, respectively. These errors are much smaller than the errors (4.8 mm and 3.5 mm) obtained by the scanner’s built-in filter. Numéro de notice : A2016-784 Affiliation des auteurs : non IGN Autre URL associée : Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.06.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82497
in ISPRS Journal of photogrammetry and remote sensing > vol 119 (September 2016) . - pp 246 - 258[article]Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features / Z. He in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
[article]
Titre : Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features Type de document : Article/Communication Auteurs : Z. He, Auteur ; Qiang Wang, Auteur ; Y. Shen, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5150 -5163 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur
[Termes IGN] décomposition en fonctions orthogonales empiriques
[Termes IGN] image hyperspectrale
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] précision de la classification
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Recently, many researchers have attempted to exploit spectral-spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral-spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral-spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples. Numéro de notice : A2014-436 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2287022 En ligne : https://doi.org/10.1109/TGRS.2013.2287022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73973
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5150 -5163[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014081B RAB Revue Centre de documentation En réserve L003 Disponible Improvement and application of the conifer forest multiangular hybrid GORT model MGeoSAIL / Qiang Wang in IEEE Transactions on geoscience and remote sensing, vol 51 n° 10 (October 2013)
[article]
Titre : Improvement and application of the conifer forest multiangular hybrid GORT model MGeoSAIL Type de document : Article/Communication Auteurs : Qiang Wang, Auteur ; Yong Pang, Auteur ; Zengyuan Li, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 5047 - 5059 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Chine
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] forêt
[Termes IGN] houppier
[Termes IGN] image PROBA-CHRIS
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de transfert radiatif
[Termes IGN] PinophytaRésumé : (Auteur) Compared with traditional remote sensing, multiangular observation provides 3-D structural information of a forest through different directional observations. The MGeoSAIL model, suitable for multiangular observations, was developed based on the single-angle model GeoSAIL. The MGeoSAIL model combines the geometric-optic model with the radiation transfer model and has the advantages of both models. Thus, it is more accurate and feasible. The geometric-optic model calculates the amount of shadowed and illuminated components within a forest scene, while the radiation transfer model [Scattering by Arbitrarily Inclined Leaves (SAIL)] calculates the reflectance and transmittance of tree crowns. The uniform index is introduced to characterize the relationship quantitatively between tree distribution pattern and the bidirectional reflectance distribution function (BRDF). The simulation results show that the MGeoSAIL model could simulate the “hot” spot in red and near-infrared bands, as well as the “bowl” shape in the near-infrared band. The relationship between the uniform index and BRDF is negatively exponential. Finally, the look-up table was calculated using the MGeoSAIL model, and leaf area index (LAI) was inversed from compact high-resolution imaging spectrometry data. The results compared well with the measured LAI in Changbai Mountain area, China. Numéro de notice : A2013-603 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2234466 En ligne : https://doi.org/10.1109/TGRS.2012.2234466 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32739
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 10 (October 2013) . - pp 5047 - 5059[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013101 RAB Revue Centre de documentation En réserve L003 Disponible