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A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation / Qing Wang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)
[article]
Titre : A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation Type de document : Article/Communication Auteurs : Qing Wang, Auteur ; Hua Sun, Auteur ; Ruopu Li, Auteur ; Guangxing Wang, Auteur Année de publication : 2019 Article en page(s) : pp 145 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] forêt
[Termes IGN] géostatistique
[Termes IGN] image Landsat-OLI
[Termes IGN] image SPOT 5
[Termes IGN] Mongolie intérieure (Chine)
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] utilisation du sol
[Termes IGN] variogrammeRésumé : (Auteur) Traditional parametric methods for classification of land use and land cover (LULC) types using remote sensing imagery assume a global distribution model and fail to consider local variation of categorical variables. Differently, non-parametric methods do not make any statistical assumptions but are typically sensitive to the sample sizes of training sample data that usually require a high cost to collect in the field. Geostatistical classifiers, such as indicator kriging and simulation, are local variability-based methods that exhibit great potential for image-based classification of LULC types. However, variogram models required are highly sensitive to the spatial configuration of training samples as well as sample size given a study area. Moreover, when a large number of spectral variables are considered into kriging systems, modeling the variograms and cross-variograms would be problematic. To circumvent these issues, this study extended the geostatistical methods from a 2-dimensional geographic space to a m-dimensional image feature space to derive feature-space indicator variograms (FSIVs). Moreover, a novel stochastic simulation classification algorithm, Feature-Space Indicator Simulation (FSIS), was proposed and examined for classification of LULC types in Duolun County located in Inner Mongolia and in Huang-Feng-Qiao (HFQ) forest farm, Hunan of China. In Duolun, six LULC types were involved and in HFQ a complicated forest landscape consisting of nine forest types plus water, built-up area, and agricultural/bare soil, was classified. The classification results of FSIS were compared with another feature-space geostatistical classifier – feature-space indicator kriging (FSIK), a traditional parametric method – maximum likelihood (ML), a widely used nonparametric method – support vector machine (SVM), and a recently popular method – random forest (RF). The results showed that compared with ML, SVM and RF, in both study areas FSIS statistically significantly increased the accuracy of the classifications by 10.0–29.9% for percentage correct and 19.0–47.6% for Kappa statistic. Compared with FSIK, FSIS also improved the classification accuracy but the accuracy increases were relatively smaller with the percentages correct of 3.5% and 7.6% and the Kappa values of 4.6% and 8.6% for Duolun and HFQ, respectively. Moreover, FSIS led to the spatial uncertainties of the classification estimates as the quality measure of the estimates. In addition, the results also demonstrated that FSIVs were sensitive to the within-class heterogeneity but not very much to the size of training samples. Overall, FSIS exhibited the greater potential to improve the classification accuracy of LULC and forest types using remote sensing image. Numéro de notice : A2019-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.011 Date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92871
in ISPRS Journal of photogrammetry and remote sensing > vol 152 (June 2019) . - pp 145 - 165[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Vehicle detection in aerial images / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 4 (avril 2019)
[article]
Titre : Vehicle detection in aerial images Type de document : Article/Communication Auteurs : Michael Ying Yang, Auteur ; Wentong Liao, Auteur ; Xinbo Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 297 - 304 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] compréhension de l'image
[Termes IGN] détection d'objet
[Termes IGN] entropie
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] précision de la classification
[Termes IGN] qualité d'image
[Termes IGN] réseau neuronal convolutif
[Termes IGN] véhicule automobileRésumé : (Auteur) The detection of vehicles in aerial images is widely applied in many applications. Comparing with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem because of small vehicle size and the complex background. In this paper, we propose a novel double focal loss convolutional neural network (DFL-CNN) framework. In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposal network (RPN) and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. We demonstrate the performance of our model on the existing benchmark German Aerospace Center (DLR) 3K dataset as well as the ITCVD dataset. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection. Numéro de notice : A2019-163 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.4.297 Date de publication en ligne : 01/04/2019 En ligne : https://doi.org/10.14358/PERS.85.4.297 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92568
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 4 (avril 2019) . - pp 297 - 304[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2019041 SL Revue Centre de documentation Revues en salle Disponible Improving LiDAR classification accuracy by contextual label smoothing in post-processing / Nan Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)
[article]
Titre : Improving LiDAR classification accuracy by contextual label smoothing in post-processing Type de document : Article/Communication Auteurs : Nan Li, Auteur ; Chun Liu, Auteur ; Norbert Pfeifer, Auteur Année de publication : 2019 Article en page(s) : pp 13 - 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] graphe
[Termes IGN] lissage de valeur
[Termes IGN] post-traitement
[Termes IGN] précision de la classification
[Termes IGN] régularisation
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsRésumé : (Auteur) We propose a contextual label-smoothing method to improve the LiDAR classification accuracy in a post-processing step. Under the framework of global graph-structured regularization, we enhance the effectiveness of label smoothing from two aspects. First, each point can collect sufficient label-relevant neighborhood information to verify its label based on an optimal graph. Second, the input label probability set is improved by probabilistic label relaxation to be more consistent with the spatial context. With this optimal graph and reliable label probability set, the final labels are computed by graph-structured regularization. We demonstrate the contextual label-smoothing approach on two separate urban airborne LiDAR datasets with complex urban scenes. Significant improvements in the classification accuracies are achieved without losing small objects (such as façades and cars). The overall accuracy is increased by 7.01% on the Vienna dataset and 6.88% on the Vaihingen dataset. Moreover, most large, wrongly labeled regions are corrected by long-range interactions that are derived from the optimal graph, and misclassified regions that lack neighborhood communications in terms of correct labels are also corrected with the probabilistic label relaxation. Numéro de notice : A2019-069 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.022 Date de publication en ligne : 13/12/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.022 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92156
in ISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 13 - 31[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt A multi-faceted CNN architecture for automatic classification of mobile LiDAR data and an algorithm to reproduce point cloud samples for enhanced training / Bhavesh Kumar in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)
[article]
Titre : A multi-faceted CNN architecture for automatic classification of mobile LiDAR data and an algorithm to reproduce point cloud samples for enhanced training Type de document : Article/Communication Auteurs : Bhavesh Kumar, Auteur ; Gaurav Pandey, Auteur ; Bharat Lohani, Auteur ; Subhas C. Misra, Auteur Année de publication : 2019 Article en page(s) : pp 80 - 89 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification automatique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] échantillonnage
[Termes IGN] image à haute résolution
[Termes IGN] précision de la classification
[Termes IGN] réseau neuronal convolutif
[Termes IGN] semis de pointsRésumé : (auteur) Mobile Laser Scanning (MLS) data of outdoor environment are typically characterised by occlusion, noise, clutter, large data size and high quantum of information which makes their classification a challenging problem. This paper presents three deep Convolutional Neural Network (CNN) architectures in three dimension (3D), namely single CNN (SCN), multi-faceted CNN (MFC) and MFC with reproduction (MFCR) for automatic classification of MLS data. The MFC uses multiple facets of an MLS sample as inputs to different SCNs, thus providing additional information during classification. The MFC, once trained, is used to reproduce additional samples with the help of existing samples. The reproduced samples are employed to further refine the MFC training parameters, thus giving a new method called MFCR. The three architectures are evaluated on an ensemble of 3D outdoor MLS data consisting of four classes, i.e. tree, pole, house and ground covered with low vegetation along with car samples from KITTI dataset. The total accuracy and kappa values of classifications reached up to (i) 86.0% and 81.3% for the SCN (ii) 94.3% and 92.4% for the MFC and (iii) 96.0% and 94.6% for the MFCR, respectively. The paper has demonstrated the use of multiple facets to significantly improve classification accuracy over the SCN. Finally, a unique approach has been developed for reproduction of samples which has shown potential to improve the accuracy of classification. Unlike previous works on the use of CNN for structured point cloud of indoor objects, this work shows the utility of different proposed CNN architectures for classification of varieties of outdoor objects, viz., tree, pole, house and ground which are captured as unstructured point cloud by MLS. Numéro de notice : A2019-034 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.006 Date de publication en ligne : 20/11/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91971
in ISPRS Journal of photogrammetry and remote sensing > vol 147 (January 2019) . - pp 80 - 89[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019011 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019013 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2019012 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Coupling relationship among scale parameter, segmentation accuracy, and classification accuracy in GeOBIA / Ming Dongping in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)
[article]
Titre : Coupling relationship among scale parameter, segmentation accuracy, and classification accuracy in GeOBIA Type de document : Article/Communication Auteurs : Ming Dongping, Auteur ; Wen Zhou, Auteur ; Xu Lu, Auteur ; Min Wang, Auteur ; Yanni Ma, Auteur Année de publication : 2018 Article en page(s) : pp 681-693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] précision de la classification
[Termes IGN] segmentation d'imageRésumé : (Auteur) The quality of multi-scale segmentation mainly consists of intrasegment homogeneity and intersegment heterogeneity; however, it is difficult to synchronously get both high. It is crucial to make it clear which one of these two measures is more important and what is the coupling relationship among segmentation scale parameter, image segmentation and classification accuracy. This paper employs series of segmentation and classification to show that (1) intrasegment homogeneity is more important than intersegment heterogeneity in GeOBIA; there is always highly positive correlation between intrasegment homogeneity and classification accuracy; (2) with the increase of spectral heterogeneity parameter, both image object amount and the intrasegment homogeneity decrease; however the intersegment heterogeneity increases or increases first then decrease after the appropriate scale; and (3) the appropriate scale means there is a compromise between intrasegment homogeneity and intersegment heterogeneity. The research findings are helpful to raise awareness among practitioners who suffer from scale issues in GeOBIA. Numéro de notice : A2018-484 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.11.681 Date de publication en ligne : 01/11/2018 En ligne : https://doi.org/10.14358/PERS.84.11.681 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91209
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 11 (November 2018) . - pp 681-693[article]Exemplaires(1)
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