Descripteur
Termes IGN > imagerie > image spatiale > image satellite > image ALOS > image ALOS-PALSAR
image ALOS-PALSARVoir aussi |
Documents disponibles dans cette catégorie (77)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Multiple-entity based classification of airborne laser scanning data in urban areas / S. Xu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
[article]
Titre : Multiple-entity based classification of airborne laser scanning data in urban areas Type de document : Article/Communication Auteurs : S. Xu, Auteur ; M. George Vosselman, Auteur ; Sander J. Oude Elberink, Auteur Année de publication : 2014 Article en page(s) : pp 1 - 15 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse multicritère
[Termes IGN] classificateur paramétrique
[Termes IGN] classification automatique d'objets
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] image ALOS-PALSAR
[Termes IGN] milieu urbain
[Termes IGN] test de performanceRésumé : (Auteur) There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method. Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified. Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers. Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information. Numéro de notice : A2014-080 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32985
in ISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 1 - 15[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Study on the polarimetric characteristics of the Lop Nur arid area using PolSAR data / Zhihong Gao in Journal of applied remote sensing, vol 8 (2014)
[article]
Titre : Study on the polarimetric characteristics of the Lop Nur arid area using PolSAR data Type de document : Article/Communication Auteurs : Zhihong Gao, Auteur ; Huaze Gong, Auteur ; Xu Zhou, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : 14 p. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] caractérisation
[Termes IGN] décomposition spectrale
[Termes IGN] données polarimétriques
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] lac
[Termes IGN] polarimétrie radar
[Termes IGN] Sinkiang (Chine)
[Termes IGN] zone arideRésumé : (auteur) The quantitative study of the arid Lop Nur lake basin is significant to investigate the environmental changes in the arid area of northwestern China and extremely arid areas of Eurasia in general. Synthetic aperture radar (SAR) imagery, with its penetration capability and advantages for studying geological phenomena on a large spatial scale, is very suitable for analyzing the subsurface of the Lop Nur area. Based on the full polarimetric ALOS PALSAR data and field investigation, it was found that the two-layer scattering mechanism of the dry sediments is very special and complex. The scattering mechanism in the bright strips is more complex than that in the gray strips according to the co-polarization correlation analysis. The experimental results show that the Cloude–Pottier decomposition method is more appropriate for this area. Moreover, the polarimetric characteristics and Cloude-Pottier decomposition results are very important for the study of the past climatic change in Lop Nur area. In conclusion, full polarimetric SAR data and target decomposition theory provide a new technique for obtaining information and quantitatively studying the subsurface characteristics of arid areas. Numéro de notice : A2014-701 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1117/1.JRS.8.083681 En ligne : http://remotesensing.spiedigitallibrary.org/article.aspx?articleid=1828549 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75390
in Journal of applied remote sensing > vol 8 (2014) . - 14 p.[article]An innovative support vector machine based method for contextual image classification / Rogério Galante Negri in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
[article]
Titre : An innovative support vector machine based method for contextual image classification Type de document : Article/Communication Auteurs : Rogério Galante Negri, Auteur ; Luciano Vieira Dutra, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 241 - 248 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification contextuelle
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moiréeRésumé : (Auteur) Several remote sensing studies have adopted the Support Vector Machine (SVM) method for image classification. Although the original formulation of the SVM method does not incorporate contextual information, there are different proposals to incorporate this type of information into it. Usually, these proposals modify the SVM training phase or make an integration of SVM classifications using stochastic models. This study presents a new perspective on the development of contextual SVMs. The main concept of this proposed method is to use the contextual information to displace the separation hyperplane, initially defined by the traditional SVM. This displaced hyperplane could cause a change of the class initially assigned to the pixel. To evaluate the classification effectiveness of the proposed method a case study is presented comparing the results with the standard SVM and the SVM post-processed by the mode (majority) filter. An ALOS/PALSAR image, PLR mode, acquired over an Amazon area was used in the experiment. Considering the inner area of test sites, the accuracy results obtained by the proposed method is better than SVM and similar to SVM post-processed by the mode filter. The proposed method, however, produces better results than mode post-processed SVM when considering the classification near the edges between regions. One drawback of the method is the computational cost of the proposed method is significantly greater than the compared methods. Numéro de notice : A2014-020 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32925
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 241 - 248[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Retrieval of tropical forest biomass information from ALOS PALSAR data / Mahmudur Rahman in Geocarto international, vol 28 n° 5-6 (August - October 2013)
[article]
Titre : Retrieval of tropical forest biomass information from ALOS PALSAR data Type de document : Article/Communication Auteurs : Mahmudur Rahman, Auteur ; Josaphat Tetuko Sri Sumantyo, Auteur Année de publication : 2013 Article en page(s) : pp 382 - 403 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] Bangladesh
[Termes IGN] biomasse
[Termes IGN] forêt tropicale
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] régression
[Termes IGN] rétrodiffusionRésumé : (Auteur) Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS PALSAR) data from different observation modes were analysed to determine (1) which observation mode most accurately retrieves tropical forest biomass information and (2) whether different modes, when considered together, yield improved results in comparison to identical data-sets analysed independently. We performed regression analysis to estimate above-ground forest biomass using PALSAR backscatter data for natural and planted forests in south-eastern Bangladesh. The coefficient of determination (r 2) was lower or equal to 0.499 (n = 70) when PALSAR data from different observation modes were separately considered, but increased sharply when one class (rubber) is dropped and average backscatter of fine beam single (FBS) and polarimetric (PLR) modes are used in the analysis. The results presented in this article are useful for both regional and global forest biomass inventories and fixing acquisition modes for planned L-band SAR missions. Numéro de notice : A2013-547 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.710652 Date de publication en ligne : 04/09/2012 En ligne : https://doi.org/10.1080/10106049.2012.710652 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32683
in Geocarto international > vol 28 n° 5-6 (August - October 2013) . - pp 382 - 403[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2013031 RAB Revue Centre de documentation En réserve L003 Disponible InSAR-derived coseismic deformation of the 2010 Southeastern Iran earthquake (M6.5) and its relationship with the tectonic background in the South of Lut Block / Tomokazu Kobayashi in Bulletin of the GeoSpatial Information authority of Japan, vol 60 (March 2013)
[article]
Titre : InSAR-derived coseismic deformation of the 2010 Southeastern Iran earthquake (M6.5) and its relationship with the tectonic background in the South of Lut Block Type de document : Article/Communication Auteurs : Tomokazu Kobayashi, Auteur ; Mikio Tobita, Auteur ; Akira Suzuki, Auteur ; Yuko Noguchi, Auteur Année de publication : 2013 Article en page(s) : pp 7 - 17 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] faille géologique
[Termes IGN] image ALOS-PALSAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Iran
[Termes IGN] séisme
[Termes IGN] tectonique des plaquesRésumé : (Auteur) An inland crustal earthquake with a magnitude of 6.5 that occurred in the southeast of Iran on December 20, 2010 ruptured an unknown fault at depth. Applying interferometric SAR (InSAR) analysis using ALOS/PALSAR data to the earthquake, we detected the coseismic signal from both ascending orbit interferogram of fine beam mode and descending orbit interferogram of ScanSAR mode. Our preferred fault model, assuming a rectangular fault with a uniform slip, shows a nearly pure dextral fault motion with NE-SW-oriented strike. The estimated moment magnitude is 6.6. The fault of the mainshock is on the southern extension of the Kahurak fault, suggesting that the causative fault of this event is probably the identical fault system to the Kahurak fault. Numéro de notice : A2013-108 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : https://www.gsi.go.jp/common/000066400.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32246
in Bulletin of the GeoSpatial Information authority of Japan > vol 60 (March 2013) . - pp 7 - 17[article]Dual-Polarimetric signatures of vegetation – a case study Biebrza / Dariusz Ziolkowski in Geoinformation issues, vol 5 n° 1 (2013)PermalinkAn underground-mining detection system based on DInSAR / Z. Hu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 2 (January 2013)PermalinkEvaluation de l'apport de la télédétection radar pour la cartographie des végétations dans le Parc du Pilat / Cécile Cazals (2013)PermalinkApports des données ALOS PALSAR polarimétriques à la détection des zones humides littorales (Sassandra, Côte d'Ivoire) / Kouakou Hervé Kouassi in Photo interprétation, European journal of applied remote sensing, vol 48 n° 4 (décembre 2012)Permalink3D coseismic displacement of 2010 Darfield, New Zealand earthquake estimated from multi-aperture InSAR and D-InSAR measurements / J. Hu in Journal of geodesy, vol 86 n° 11 (November 2012)PermalinkMapping tropical forests and rubber plantations in complex landscapes by integrating PALSAR and MODIS imagery / J. Dong in ISPRS Journal of photogrammetry and remote sensing, vol 74 (Novembrer 2012)PermalinkMonitoring ground subsidence in Shanghai maglev area using two kinds of SAR data / J. Wu in Journal of applied geodesy, vol 6 n° 3-4 (November 2012)PermalinkDetecting depolarized targets using a new geometrical perturbation filter / Armando Marino in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 1 (October 2012)PermalinkQuantifying deforestation in the Brazilian Amazon using advanced land observing satellite phased array L-band synthetic aperture radar (ALOS PALSAR) and shuttle imaging radar (SIR)-C data / M. Rahman in Geocarto international, vol 27 n° 6 (October 2012)PermalinkA comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region / Dong Lu ; E. Moran ; et al. in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)PermalinkIonospheric artifacts in simultaneous L-band InSAR and GPS observations / J. Chen in IEEE Transactions on geoscience and remote sensing, vol 50 n° 4 (April 2012)PermalinkPotential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation / M. Sarker in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)PermalinkCharacterization of forests and deforestation in Cambodia using ALOS/PALSAR observation / R. Avtar in Geocarto international, vol 27 n° 2 (March 2012)PermalinkJoint processing of Landsat and ALOS-PALSAR data for forest mapping and monitoring / E. Lehmann in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)PermalinkLong-term consecutive DInSAR for volume change estimation of land deformation / S. Sumantyo in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)PermalinkThe crustal deformation and fault model of the 2011 off the Pacific coast of Tohoku earthquake / T. Imakiire in Bulletin of the GeoSpatial Information authority of Japan, vol 59 (December 2011)PermalinkElectromagnetic land surface classification through integration of optical and radar remote sensing data / J. Baek in IEEE Transactions on geoscience and remote sensing, vol 49 n° 4 (April 2011)PermalinkGeodetically accurate InSAR data processor / H. Zebker in IEEE Transactions on geoscience and remote sensing, vol 48 n° 12 (December 2010)PermalinkMulti-path PALSAR interferometric observations of the 2008 magnitude 8.0 Wenchuan earthquake / K. Zhang in International Journal of Remote Sensing IJRS, vol 31 n° 13 (July /2010)Permalinkvol 31 n° 13 - July /2010 - Special issue : Satellite observations of the Wenchuan earthquake of 12 may 2008 (Bulletin de International Journal of Remote Sensing IJRS) / Ranjit SinghPermalink