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Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification / Markus Gerke in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
[article]
Titre : Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification Type de document : Article/Communication Auteurs : Markus Gerke, Auteur ; Jing Xiao, Auteur Année de publication : 2014 Article en page(s) : pp 78 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] classification par arbre de décision
[Termes IGN] conflation
[Termes IGN] densification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] semis de points
[Termes IGN] toit
[Termes IGN] voxelRésumé : (Auteur) Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of this paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy problems related to large ALS point spacing. Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline. Numéro de notice : A2014-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32919
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 78 - 92[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Modeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using Geoinformatic techniques / M. Sarabuddin Mondal in Geocarto international, vol 28 n° 7-8 (November - December 2013)
[article]
Titre : Modeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using Geoinformatic techniques Type de document : Article/Communication Auteurs : M. Sarabuddin Mondal, Auteur ; Nayan Sharma, Auteur ; Martin Kappas, Auteur ; Pradeep Kumar Garg, Auteur Année de publication : 2013 Article en page(s) : pp 632 - 656 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] bassin hydrographique
[Termes IGN] Brahmapoutre (fleuve)
[Termes IGN] carte d'occupation du sol
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification dirigée
[Termes IGN] image multitemporelle
[Termes IGN] Inde
[Termes IGN] modèle conceptuel de données spatio-temporelles
[Termes IGN] occupation du sol
[Termes IGN] processus spatio-temorel
[Termes IGN] utilisation du solRésumé : (Auteur) An attempt has been made to explore and evaluate the Cellular Automata (CA) Markov modelling to monitor and predict the future land use and land cover (LULC) scenario in a part of Brahmaputra River basin using LULC maps derived from multi-temporal satellite images. CA Markov is a combined cellular automata/Markov chain/multi-criteria/multi-objective land allocation (MOLA) LULC prediction procedure that adds an element of spatial contiguity as well as knowledge base of the likely spatial distribution of transitions to Markov chain analysis. Evidence likelihood map was used for as knowledge base of the likely spatial procedure in CA Markov model. The predicting quantity and predicting location change have been analysed and statistically evaluated. The validation statistics indicated how well the comparison map agreed and disagreed with the reference map. Predicted results accuracy is slightly higher when compare to others studies of LULC change using CA Markov approaches. Numéro de notice : A2013-701 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2013.776641 Date de publication en ligne : 01/08/2013 En ligne : https://doi.org/10.1080/10106049.2013.776641 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32837
in Geocarto international > vol 28 n° 7-8 (November - December 2013) . - pp 632 - 656[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2013041 RAB Revue Centre de documentation En réserve L003 Disponible Integrated denoising and unwrapping of INSAR phase based on Markov random fields / Runpu Chen in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)
[article]
Titre : Integrated denoising and unwrapping of INSAR phase based on Markov random fields Type de document : Article/Communication Auteurs : Runpu Chen, Auteur ; Weidong Yu, Auteur ; Robert Wang, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4473 - 4485 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] champ aléatoire de Markov
[Termes IGN] filtrage du bruit
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] phase
[Termes IGN] reconstruction d'image
[Termes IGN] restauration d'imageRésumé : (Auteur) In the traditional processing flow of interferometric synthetic aperture radar (SAR) technique, the processing of phase is conducted via two separated and successive steps, i.e., phase denoising and phase unwrapping. That is to say, first, wrapped phases without noise are generated, and then, the true phases without 2?-ambiguities are reconstructed (here and in the rest of this paper, true phase refers to the information-induced unwrapped phase without noise). Such separated steps will inevitably bring in extra estimation error because each step has necessary approximations and presumptions which do not always hold. On the contrary, in this paper, we treat phase denoising and unwrapping as a single problem of true phase recovery from observed ones. Following this methodology, an integrated phase denoising and unwrapping algorithm based upon Markov random fields (MRFs) is proposed. Taking a priori knowledge of interferometric phases into account, MRF is used to model the relationship between the elements in the random variable set including both true phases and their observations. After the model is built up, the energy function of this MRF is defined according to the local-independence property inferred from the MRF structure and then minimized to obtain the estimate of the true phase value. In the end of this paper, experiments on simulated and true phase data are conducted, and the comparison with several commonly used unwrapping methods is proposed to verify the efficiency of the proposed MRF algorithm. Numéro de notice : A2013-419 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2268969 En ligne : https://doi.org/10.1109/TGRS.2013.2268969 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32557
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 8 (August 2013) . - pp 4473 - 4485[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013081 RAB Revue Centre de documentation En réserve L003 Disponible Multiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 2 (April 2013)
[article]
Titre : Multiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery Type de document : Article/Communication Auteurs : Ping Zhong, Auteur ; Runsheng Wang, Auteur Année de publication : 2013 Article en page(s) : pp 2260 - 2275 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] spectroscopieRésumé : (Auteur) Denoising of hyperspectral imagery in the domain of imaging spectroscopy by conditional random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the strong dependencies across spatial and spectral neighbors have been proved to be very useful. Many available hyperspectral image denoising algorithms adopt multidimensional tools to deal with the problems and thus naturally focus on the use of the spectral dependencies. However, few of them were specifically designed to use the spatial dependencies. In this paper, we propose a multiple-spectral-band CRF (MSB-CRF) to simultaneously model and use the spatial and spectral dependencies in a unified probabilistic framework. Furthermore, under the proposed MSB-CRF framework, we develop two hyperspectral image denoising algorithms, which, thanks to the incorporated spatial and spectral dependencies, can significantly remove the noise, while maintaining the important image details. The experiments are conducted in both simulated and real noisy conditions to test the proposed denoising algorithms, which are shown to outperform the popular denoising methods described in the previous literatures Numéro de notice : A2013-224 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2209656 En ligne : https://doi.org/10.1109/TGRS.2012.2209656 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32362
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 4 Tome 2 (April 2013) . - pp 2260 - 2275[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013041B RAB Revue Centre de documentation En réserve L003 Disponible A graph-based classification method for hyperspectral images / J. Bai in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)
[article]
Titre : A graph-based classification method for hyperspectral images Type de document : Article/Communication Auteurs : J. Bai, Auteur ; S. Xiang, Auteur ; C. Pan, Auteur Année de publication : 2013 Article en page(s) : pp 803 - 817 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme Graph-Cut
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'image
[Termes IGN] signature spectraleRésumé : (Auteur) The goal of this paper is to apply graph cut (GC) theory to the classification of hyperspectral remote sensing images. The task is formulated as a labeling problem on Markov random field (MRF) constructed on the image grid, and GC algorithm is employed to solve this task. In general, a large number of user interactive strikes are necessary to obtain satisfactory segmentation results. Due to the spatial variability of spectral signatures, however, hyperspectral remote sensing images often contain many tiny regions. Labeling all these tiny regions usually needs expensive human labor. To overcome this difficulty, a pixelwise fuzzy classification based on support vector machine (SVM) is first applied. As a result, only pixels with high probabilities are preserved as labeled ones. This generates a pseudouser strike map. This map is then employed for GC to evaluate the truthful likelihoods of class labels and propagate them to the MRF. To evaluate the robustness of our method, we have tested our method on both large and small training sets. Additionally, comparisons are made between the results of SVM, SVM with stacking neighboring vectors, SVM with morphological preprocessing, extraction and classification of homogeneous objects, and our method. Comparative experimental results demonstrate the validity of our method. Numéro de notice : A2013-081 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2205002 En ligne : https://doi.org/10.1109/TGRS.2012.2205002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32219
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 803 - 817[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013021 RAB Revue Centre de documentation En réserve L003 Disponible A hybrid multiview stereo algorithm for modeling urban scenes / Florent Lafarge in IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, vol 35 n° 1 (January 2013)PermalinkCreating large-scale city models from 3D-Point clouds : a robust approach with hybrid representation / Florent Lafarge in International journal of computer vision, vol 99 n° 1 (August 2012)PermalinkAn automated approach for updating land cover maps based on integrated change detection and classification methods / X. Chen in ISPRS Journal of photogrammetry and remote sensing, vol 71 (July 2012)PermalinkEvaluation of bayesian despeckling and texture extraction methods based on Gauss–Markov and auto-binomial gibbs random fields: Application to TerraSAR-X data / D. Espinoza Molina in IEEE Transactions on geoscience and remote sensing, vol 50 n° 5 Tome 2 (May 2012)PermalinkExtraction of building roof contours from LiDAR data using a Markov-random-field-based approach / E. Dos Santos Galvanin in IEEE Transactions on geoscience and remote sensing, vol 50 n° 3 (March 2012)PermalinkPermalinkConditional random fields for urban scene : Classification with full waveform LiDAR Data / Joachim Niemeyer (2011)PermalinkUse Markov random fields for automatic cloud-shadow detection on high resolution / Sylvie Le Hégarat-Mascle in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)PermalinkTexture feature fusion with neighborhood oscillating tabu search for high resolution image classification / L. Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 3 (March 2008)PermalinkModélisation et statistique spatiales / Carlo Gaetan (2008)Permalink