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A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data / Chuanfa Chen in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)
[article]
Titre : A multiresolution hierarchical classification algorithm for filtering airborne LiDAR data Type de document : Article/Communication Auteurs : Chuanfa Chen, Auteur Année de publication : 2013 Article en page(s) : pp 1 - 9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse multirésolution
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtrage de points
[Termes IGN] interpolation
[Termes IGN] Kappa de Cohen
[Termes IGN] semis de points
[Termes IGN] seuillage de pointsRésumé : (Auteur) We presented a multiresolution hierarchical classification (MHC) algorithm for differentiating ground from non-ground LiDAR point cloud based on point residuals from the interpolated raster surface. MHC includes three levels of hierarchy, with the simultaneous increase of cell resolution and residual threshold from the low to the high level of the hierarchy. At each level, the surface is iteratively interpolated towards the ground using thin plate spline (TPS) until no ground points are classified, and the classified ground points are used to update the surface in the next iteration. 15 groups of benchmark dataset, provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission, were used to compare the performance of MHC with those of the 17 other publicized filtering methods. Results indicated that MHC with the average total error and average Cohen’s kappa coefficient of 4.11% and 86.27% performs better than all other filtering methods. Numéro de notice : A2013-407 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.05.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.05.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32545
in ISPRS Journal of photogrammetry and remote sensing > vol 82 (August 2013) . - pp 1 - 9[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013081 RAB Revue Centre de documentation En réserve L003 Disponible Graph-based feature selection for object-oriented classification in VHR airborne imagery / Tianen Chen in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 2 (January 2011)
[article]
Titre : Graph-based feature selection for object-oriented classification in VHR airborne imagery Type de document : Article/Communication Auteurs : Tianen Chen, Auteur ; Tian Fang, Auteur ; H. Huo, Auteur ; D. Li, Auteur Année de publication : 2011 Article en page(s) : pp 353 - 365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification dirigée
[Termes IGN] classification orientée objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] graphe
[Termes IGN] image à ultra haute résolution
[Termes IGN] image aérienne
[Termes IGN] Kappa de Cohen
[Termes IGN] matrice
[Termes IGN] pondération
[Termes IGN] similitude
[Termes IGN] voisinage (relation topologique)Résumé : (Auteur) Linearly nonseparability and class imbalance of very high resolution (VHR) imagery make feature selection for object-oriented classification quite challenging, while such characteristics, especially class imbalance, have usually been ignored in open literature. To cope with the challenges, this paper proposes a new graph-based feature selection method named locally weighted discriminating projection (LWDP). First, the popular graph-based criteria of feature selection are reformulated to present linear or nonlinear mapping in feature space. Second, weight matrices of graphs characterize dissimilarity rather than similarity between pairwise neighbors, to well-preserved local structure when the difference of distance between a sample and its neighbors is large. Finally, LWDP provides a new perspective to alleviate class imbalance at both global and local levels, by restricting the pairwise relationships in the weight matrices. Specifically, neighborhood unions are introduced to employ the local class distribution and class size to constrain pairwise relationships in the weight matrices when classifying unbalanced sample sets. To evaluate the performances of LWDP in low dimensions, a holistic scoring scheme is proposed to stress the performances under low dimensions. In addition, overall accuracy curves and Kappa Index of Agreement (KIA) curves, which exhibit KIA in dimensions, are also used. The experimental results show that LWDP and its kernel extension outperform the other classic or latest methods in processing unbalanced sample set of VHR airborne imagery. Numéro de notice : A2011-051 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2054832 Date de publication en ligne : 12/08/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2054832 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30832
in IEEE Transactions on geoscience and remote sensing > vol 49 n° 1 Tome 2 (January 2011) . - pp 353 - 365[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2011011B RAB Revue Centre de documentation En réserve L003 Disponible Consistency of accuracy assessment indices for soft classification: simulation analysis / J. Chen in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 2 (March - April 2010)
[article]
Titre : Consistency of accuracy assessment indices for soft classification: simulation analysis Type de document : Article/Communication Auteurs : J. Chen, Auteur ; X. Zhu, Auteur ; H. Imura, Auteur ; X. Chen, Auteur Année de publication : 2010 Article en page(s) : pp 156 - 164 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] classification
[Termes IGN] erreur moyenne quadratique
[Termes IGN] estimation de précision
[Termes IGN] Kappa de Cohen
[Termes IGN] matrice de confusion
[Termes IGN] niveau d'analyse
[Termes IGN] précision cartographique
[Termes IGN] précision décimétrique
[Termes IGN] simulationRésumé : (Auteur) Accuracy assessment plays a crucial role in the implementation of soft classification. Even though many indices of accuracy assessment for soft classification have been proposed, the consistencies among these indices are not clear, and the impact of sample size on these consistencies has not been investigated. This paper examines two kinds of indices: map-level indices, including root mean square error (rmse), kappa, and overall accuracy (oa) from the sub-pixel confusion matrix (SCM); and category-level indices, including crmse, user accuracy (ua) and producer accuracy (pa). A careful simulation was conducted to investigate the consistency of these indices and the effect of sample size. The major findings were as follows: (1) The map-level indices are highly consistent with each other, whereas the category-level indices are not. (2) The consistency among map-level and category-level indices becomes weaker when the sample size decreases. (3) The rmse is more affected by error distribution among classes than are kappa and oa. Based on these results, we recommend that rmse can be used for map-level accuracy due to its simplicity, although kappa and oa may be better alternatives when the sample size is limited because the two indices are affected less by the error distribution among classes. We also suggest that crmse should be provided when map users are not concerned about the error source, whereas ua and pa are more useful when the complete information about different errors is required. The results of this study will be of benefit to the development and application of soft classifiers. Copyright ISPRS Numéro de notice : A2010-090 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2009.10.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2009.10.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30286
in ISPRS Journal of photogrammetry and remote sensing > vol 65 n° 2 (March - April 2010) . - pp 156 - 164[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2010021 SL Revue Centre de documentation Revues en salle Disponible Land-cover change detection using one-class support vector machine / P. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 3 (March 2010)
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Titre : Land-cover change detection using one-class support vector machine Type de document : Article/Communication Auteurs : P. Li, Auteur ; H. Xu, Auteur Année de publication : 2010 Article en page(s) : pp 255 - 263 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de changement
[Termes IGN] données multitemporelles
[Termes IGN] Kappa de Cohen
[Termes IGN] occupation du sol
[Termes IGN] traitement d'imageRésumé : (Auteur) Change detection using remote sensing has considerable potential for monitoring land-cover change. Commonly, one specific class of change is of interest in many applications. In this paper, a recently developed one-class classifier, the One-Class Support Vector Machine (OCSVM), is proposed for the change detection of one specific class by multitemporal classification. The classifier only requires samples from the change class of interest as the training data. The performance of the proposed method was evaluated in two applications by comparing with conventional post-classification comparison methods. The results demonstrated the proposed method achieved both higher overall accuracy and higher kappa coefficient than the conventional methods, and demonstrated good potential for further application. The study also indicated that with the ocsvm, the analysis can focus only on the specific class of interest and does not need to treat other classes, thus providing highly accurate change detection. The OCSVM-based change detection method, as a general and easily implemented method, can be used for applications where only the change of one specific class is of interest. Copyright ASPRS Numéro de notice : A2010-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.76.3.255 En ligne : https://doi.org/10.14358/PERS.76.3.255 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30283
in Photogrammetric Engineering & Remote Sensing, PERS > vol 76 n° 3 (March 2010) . - pp 255 - 263[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)
[article]
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
in Photogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 4 (April 2009) . - pp 425 - 435[article]An improved fuzzy Kappa statistic that accounts for spatial autocorrelation / Alex Hagen-Zanker in International journal of geographical information science IJGIS, vol 23 n° 1-2 (january 2009)PermalinkA multi-directional ground filtering algorithm for airborne LIDAR / X. Meng in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 1 (January - February 2009)PermalinkMapping dominant vegetation communities at Meili Snow Mountain, Yunnan Province, China using satellite imagery and plant community data / Z. Zhang in Geocarto international, vol 23 n° 2 (April - May 2008)PermalinkMultisource classification using Support Vector Machines: an empirical comparison with Decision Tree and Neural Network classifiers / P. Watanachaturaporn in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 2 (February 2008)PermalinkClassification of floodplain vegetation by data fusion of spectral (CASI) and LiDAR data / G.W. Geerling in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)PermalinkMultispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation / A. Agrawal in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)PermalinkSpatial-temporal specific neighbourhood rules for cellular automata land-use modelling / Stan Geertman in International journal of geographical information science IJGIS, vol 21 n° 5 (may 2007)PermalinkUrban land-use classification using variogram-based analysis with an aerial photograph / S.S. Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 7 (July 2006)PermalinkComparing accuracy assessments to infer superiority of image classification methods / J. De Leleuw in International Journal of Remote Sensing IJRS, vol 27 n°1-2 (January 2006)PermalinkEtude de différents facteurs influant les classifications d'images multi-résolution / F. Kazemipour (2006)Permalink