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Fusion of airborne LiDAR with multispectral SPOT 5 image for enhancement of feature extraction using dempster–shafer theory / Vahideh Saeidi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)
[article]
Titre : Fusion of airborne LiDAR with multispectral SPOT 5 image for enhancement of feature extraction using dempster–shafer theory Type de document : Article/Communication Auteurs : Vahideh Saeidi, Auteur ; Biswajeet Pradhan, Auteur ; O. Idrees Mohammed, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 6017 - 6025 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] données lidar
[Termes IGN] fusion d'images
[Termes IGN] image SPOT 5
[Termes IGN] indice de végétation
[Termes IGN] théorie de Dempster-ShaferRésumé : (Auteur) This paper presents an application of data-driven Dempster-Shafer theory (DST) of evidence to fuse multisensor data for land-cover feature extraction. Over the years, researchers have focused on DST for a variety of applications. However, less attention has been given to generate and interpret probability, certainty, and conflict maps. Moreover, quantitative assessment of DST performance is often overlooked. In this paper, for implementation of DST, two main types of data were used: multisensor data such as Light Detection and Ranging (LiDAR) and multispectral satellite imagery [Satellite Pour l'Observation de la Terre 5 (SPOT 5)]. The objectives are to classify land-cover types from fused multisensor data using DST, to quantitatively assess the accuracy of the classification, and to examine the potential of slope data derived from LiDAR for feature detection. First, we derived the normalized difference vegetation index (NDVI) from SPOT 5 image and the normalized digital surface model (DSM) (nDSM) from LiDAR by subtracting the digital terrain model from the DSM. The two products were fused using the DST algorithm, and the accuracy of the classification was assessed. Second, we generated a surface slope from LiDAR and fused it with NDVI. Subsequently, the classification accuracy was assessed using an IKONOS image of the study area as ground truth data. From the two processing stages, the NDVI/nDSM fusion had an overall accuracy of 88.7%, while the NDVI/slope fusion had 75.3%. The result indicates that NDVI/nDSM integration performed better than NDVI/slope. Although the overall accuracy of the former is better than the latter (NDVI/slope), the contribution of individual class reveals that building extraction from fused slope and NDVI performed poorly. This study proves that DST is a time- and cost-effective method for accurate land-cover feature identification and extraction without the need for a prior knowledge of the scene. Furthermore, the ability to generate other products like certainty, conflict, and maximum probability maps for better visual understanding of the decision process makes it more reliable for applications such as urban planning, forest management, 3-D feature extraction, and map updating. Numéro de notice : A2014-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2294398 En ligne : https://doi.org/10.1109/TGRS.2013.2294398 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74077
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 10 tome 1 (October 2014) . - pp 6017 - 6025[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014101A RAB Revue Centre de documentation En réserve L003 Disponible Quantification of L-band InSAR coherence over volcanic areas using LiDAR and in situ measurements / Mélanie Arab-Sedze in Remote sensing of environment, vol 152 (September 2014)
[article]
Titre : Quantification of L-band InSAR coherence over volcanic areas using LiDAR and in situ measurements Type de document : Article/Communication Auteurs : Mélanie Arab-Sedze, Auteur ; Essam Heggy, Auteur ; Frédéric Bretar, Auteur ; Daniel Berveiller, Auteur ; Stéphane Jacquemoud, Auteur Année de publication : 2014 Article en page(s) : pp 202 - 216 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image SPOT 5
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Leaf Area Index
[Termes IGN] Piton de la Fournaise (volcan)
[Termes IGN] propriété diélectrique
[Termes IGN] Réunion, île de la
[Termes IGN] volcanRésumé : (auteur) Interferometric Synthetic Aperture Radar (InSAR) is a powerful tool to monitor large-scale ground deformation at active volcanoes. However, vegetation and pyroclastic deposits degrade the radar coherence and therefore the measurement of 3-D surface displacements. In this article, we explore the complementarity between ALOS–PALSAR coherence images, airborne LiDAR data and in situ measurements acquired over the Piton de La Fournaise volcano (Reunion Island, France) to determine the sources of errors that may affect repeat-pass InSAR measurements. We investigate three types of surfaces: terrains covered with vegetation, lava flows (a′a, pahoehoe or slabby pahoehoe lava flows) and pyroclastic deposits (lapilli). To explain the loss of coherence observed over the Dolomieu crater between 2008 and 2009, we first use laser altimetry data to map topographic variations. The LiDAR intensity, which depends on surface reflectance, also provides ancillary information about the potential sources of coherence loss. In addition, surface roughness and rock dielectric properties of each terrain have been determined in situ to better understand how electromagnetic waves interact with such media: rough and porous surfaces, such as the a′a lava flows, produce a higher coherence loss than smoother surfaces, such as the pahoehoe lava flows. Variations in dielectric properties suggest a higher penetration depth in pyroclasts than in lava flows at L-band frequency. Decorrelation over the lapilli is hence mainly caused by volumetric effects. Finally, a map of LAI (Leaf Area Index) produced using SPOT 5 imagery allows us to quantify the effect of vegetation density: radar coherence is negatively correlated with LAI and is unreliable for values higher than 7.5. Numéro de notice : A2014-812 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2014.06.011 Date de publication en ligne : 11/07/2014 En ligne : https://doi.org/10.1016/j.rse.2014.06.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89039
in Remote sensing of environment > vol 152 (September 2014) . - pp 202 - 216[article]An effective morphological index in automatic recognition of built-up area suitable for high spatial resolution images as ALOS and SPOT data / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)
[article]
Titre : An effective morphological index in automatic recognition of built-up area suitable for high spatial resolution images as ALOS and SPOT data Type de document : Article/Communication Auteurs : Bo Yu, Auteur ; Li Wang, Auteur ; Zheng Niu, Auteur ; Muhammad Shakir, Auteur Année de publication : 2014 Article en page(s) : pp 529 - 536 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection du bâti
[Termes IGN] image ALOS
[Termes IGN] image SPOT
[Termes IGN] indice de détection
[Termes IGN] morphologie mathématique
[Termes IGN] Normalized Difference Vegetation IndexRésumé : (Auteur) Building detection from remote sensed images is the main technique to monitor economic or environmental development of an area. Advanced Land Observing Satellite (alos) and SPOT data are reliable sources due to the limitation of weather, position, time, and other practical reasons. However, to the best of our knowledge, algorithms proposed in the identification of buildings mostly aim only at images with very high spatial resolution or high spectral resolution. There are few algorithms for detecting buildings from ALOS and SPOT data. A built-up detection index (BDI) is proposed in this paper to automatically identify buildings from images with 10 meters resolution. It synthesizes morphological theory and normalized differential vegetation index (NDVl) to enhance buildings by suppressing vegetation. Four images of ALOS and SPOT are used to verify the efficiency, stability and accuracy of BDI. Experiments show that BDI is suitable to detect buildings from 10 meters resolution with reliable accuracy. Numéro de notice : A2014-292 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.6.529-536 En ligne : https://doi.org/10.14358/PERS.80.6.529-536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33195
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 6 (June 2014) . - pp 529 - 536[article]Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain / Linlin Lu in Geocarto international, vol 29 n° 3 - 4 (June - July 2014)
[article]
Titre : Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain Type de document : Article/Communication Auteurs : Linlin Lu, Auteur ; Cuizhen Wang, Auteur ; Huadong Guo, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp. 244 - 255 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] agriculture
[Termes IGN] blé (céréale)
[Termes IGN] Chine
[Termes IGN] image SPOT-Végétation
[Termes IGN] phénologie
[Termes IGN] série temporelleRésumé : (Auteur)Monitoring phenological change in agricultural land improves our understanding of the adaptation of crops to a warmer climate. Winter wheat–maize and winter wheat–cotton double-cropping are practised in most agricultural areas in the North China Plain. A curve-fitting method is presented to derive winter wheat phenology from SPOT-VEGETATION S10 normalized difference vegetation index (NDVI) data products. The method uses a double-Gaussian model to extract two phenological metrics, the start of season (SOS) and the time of maximum NDVI (MAXT). The results are compared with phenological records at local agrometeorological stations. The SOS and MAXT have close agreement with in situ observations of the jointing date and milk-in-kernel date respectively. The phenological metrics detected show spatial variations that are consistent with known phenological characteristics. This study indicates that time-series analysis with satellite data could be an effective tool for monitoring the phenology of crops and its spatial distribution in a large agricultural region. Numéro de notice : A2014-338 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.760004 En ligne : https://doi.org/10.1080/10106049.2012.760004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73706
in Geocarto international > vol 29 n° 3 - 4 (June - July 2014) . - pp. 244 - 255[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Assessment of the image misregistration effects on object-based change detection / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
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Titre : Assessment of the image misregistration effects on object-based change detection Type de document : Article/Communication Auteurs : Gang Chen, Auteur ; Kaiguang Zhao, Auteur ; Ryan Powers, Auteur Année de publication : 2014 Article en page(s) : pp 19 - 27 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] classification orientée objet
[Termes IGN] détection de changement
[Termes IGN] estimation de précision
[Termes IGN] image multitemporelle
[Termes IGN] image SPOT 5Résumé : (Auteur) High-spatial resolution remote sensing imagery provides unique opportunities for detailed characterization and monitoring of landscape dynamics. To better handle such data sets, change detection using the object-based paradigm, i.e., object-based change detection (OBCD), have demonstrated improved performances over the classic pixel-based paradigm. However, image registration remains a critical pre-process, with new challenges arising, because objects in OBCD are of various sizes and shapes. In this study, we quantified the effects of misregistration on OBCD using high-spatial resolution SPOT 5 imagery (5 m) for three types of landscapes dominated by urban, suburban and rural features, representing diverse geographic objects. The experiments were conducted in four steps: (i) Images were purposely shifted to simulate the misregistration effect. (ii) Image differencing change detection was employed to generate difference images with all the image-objects projected to a feature space consisting of both spectral and texture variables. (iii) The changes were extracted using the Mahalanobis distance and a change ratio. (iv) The results were compared to the ‘real’ changes from the image pairs that contained no purposely introduced registration error. A pixel-based change detection method using similar steps was also developed for comparisons. Results indicate that misregistration had a relatively low impact on object size and shape for most areas. When the landscape is comprised of small mean object sizes (e.g., in urban and suburban areas), the mean size of ‘change’ objects was smaller than the mean of all objects and their size discrepancy became larger with the decrease in object size. Compared to the results using the pixel-based paradigm, OBCD was less sensitive to the misregistration effect, and the sensitivity further decreased with an increase in local mean object size. However, high-spatial resolution images typically have higher spectral variability within neighboring pixels than the relatively low resolution datasets. As a result, accurate image registration remains crucial to change detection even if an object-based approach is used. Numéro de notice : A2014-008 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32913
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 19 - 27[article]Réservation
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