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A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions / Xiaodong Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
[article]
Titre : A superresolution land-cover change detection method using remotely sensed images with different spatial resolutions Type de document : Article/Communication Auteurs : Xiaodong Li, Auteur ; Feng Ling, Auteur ; Giles M. Foody, Auteur ; Yun Du, Auteur Année de publication : 2016 Article en page(s) : pp 3822 - 3841 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification pixellaire
[Termes IGN] détection de changement
[Termes IGN] image à moyenne résolution
[Termes IGN] image à très haute résolution
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] image Terra-MODIS
[Termes IGN] itérationRésumé : (auteur) The development of remote sensing has enabled the acquisition of information on land-cover change at different spatial scales. However, a trade-off between spatial and temporal resolutions normally exists. Fine-spatial-resolution images have low temporal resolutions, whereas coarse spatial resolution images have high temporal repetition rates. A novel super-resolution change detection method (SRCD) is proposed to detect land-cover changes at both fine spatial and temporal resolutions with the use of a coarse-resolution image and a fine-resolution land-cover map acquired at different times. SRCD is an iterative method that involves endmember estimation, spectral unmixing, land-cover fraction change detection, and super-resolution land-cover mapping. Both the land-cover change/no-change map and from–to change map at fine spatial resolution can be generated by SRCD. In this study, SRCD was applied to synthetic multispectral image, Moderate-Resolution Imaging Spectroradiometer (MODIS) multispectral image and Landsat-8 Operational Land Imager (OLI) multispectral image. The land-cover from–to change maps are found to have the highest overall accuracy (higher than 85%) in all the three experiments. Most of the changed land-cover patches, which were larger than the coarse-resolution pixel, were correctly detected. Numéro de notice : A2016--122 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2528583 En ligne : https://doi.org/10.1109/TGRS.2016.2528583 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84900
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 3822 - 3841[article]A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery / Bei Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)
[article]
Titre : A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery Type de document : Article/Communication Auteurs : Bei Zhao, Auteur ; Yanfei Zhong, Auteur ; Liangpei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 73 – 85 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur paramétrique
[Termes IGN] classification automatique
[Termes IGN] classification dirigée
[Termes IGN] exitance spectrale
[Termes IGN] image à très haute résolution
[Termes IGN] mécanique statistique
[Termes IGN] modèle logique de donnéesRésumé : (auteur) Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral–structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental results show that the whole image as the scope of the pooling operator performs better than the scope generated by SP. In addition, SSBFC codes and pools the spectral and structural features separately to avoid mutual interruption between the spectral and structural features. The coding vectors of spectral and structural features are then concatenated into a final coding vector. Finally, SSBFC classifies the final coding vector by support vector machine (SVM) with a histogram intersection kernel (HIK). Compared with the latest scene classification methods, the experimental results with three VHSR datasets demonstrate that the proposed SSBFC performs better than the other classification methods for VHSR image scenes. Numéro de notice : A2016-579 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.03.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81718
in ISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 73 – 85[article]Supervised classification of very high resolution optical images using wavelet-based textural features / Olivier Regniers in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
[article]
Titre : Supervised classification of very high resolution optical images using wavelet-based textural features Type de document : Article/Communication Auteurs : Olivier Regniers, Auteur ; Lionel Bombrun, Auteur ; Virginie Lafon, Auteur ; Christian Germain, Auteur Année de publication : 2016 Article en page(s) : pp 3722 - 3735 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multivariée
[Termes IGN] classification dirigée
[Termes IGN] extraction automatique
[Termes IGN] image à très haute résolution
[Termes IGN] image optique
[Termes IGN] image panchromatique
[Termes IGN] image Pléiades
[Termes IGN] texture d'imageRésumé : (Auteur) In this paper, we explore the potentialities of using wavelet-based multivariate models for the classification of very high resolution optical images. A strategy is proposed to apply these models in a supervised classification framework. This strategy includes a content-based image retrieval analysis applied on a texture database prior to the classification in order to identify which multivariate model performs the best in the context of application. Once identified, the best models are further applied in a supervised classification procedure by extracting texture features from a learning database and from regions obtained by a presegmentation of the image to classify. The classification is then operated according to the decision rules of the chosen classifier. The use of the proposed strategy is illustrated in two real case applications using Pléiades panchromatic images: the detection of vineyards and the detection of cultivated oyster fields. In both cases, at least one of the tested multivariate models displays higher classification accuracies than gray-level cooccurrence matrix descriptors. Its high adaptability and the low number of parameters to be set are other advantages of the proposed approach. Numéro de notice : A2016-858 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2526078 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2526078 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83002
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3722 - 3735[article]Autonomous ortho-rectification of very high resolution imagery using SIFT and genetic algorithm / Pramod Kumar Konugurthi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)
[article]
Titre : Autonomous ortho-rectification of very high resolution imagery using SIFT and genetic algorithm Type de document : Article/Communication Auteurs : Pramod Kumar Konugurthi, Auteur ; Raghavendra Kune, Auteur ; Ravi Nooka, Auteur ; Venkatraman Sarma, Auteur Année de publication : 2016 Article en page(s) : pp 377 - 388 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] algorithme génétique
[Termes IGN] appariement d'images
[Termes IGN] chaîne de traitement
[Termes IGN] image à très haute résolution
[Termes IGN] orthorectification
[Termes IGN] point d'appui
[Termes IGN] SIFT (algorithme)Résumé : (Auteur) Ortho-rectification of very high resolution imagery from agile platforms using Rigorous Sensor Model / Rational Functional Model is quite challenging and demands a fair amount of interactivity in Ground Control Point (GCP) identification/selection for refining the model and for final product evaluation. The paper proposes achieving complete automation in the ortho-rectification process by eliminating all the interactive components, and incorporating fault tolerance mechanisms within the model to make the process robust and reliable. The key aspects proposed in this paper are: two stage Scale Invariant Feature Transform (SIFT) based matching to obtain a large numbers of checkpoints using much coarser resolution images such as Landsat/ETM+, followed by a GA to select the right combination of minimal GCPS based on minimizing Root Mean Square Error (RMSE) and maximizing the area covered under GCPS, and finally, a decision rule based product evaluation to make the process operate in an "autonomous closed loop mode". The method is generic and has been tested on hundreds of Cartosat-1/2 images, and has achieved above 90% reliability with sub-pixel relative error of reference data. Numéro de notice : A2016-412 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.5.377 En ligne : http://dx.doi.org/10.14358/PERS.82.5.377 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81279
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 5 (May 2016) . - pp 377 - 388[article]Forest stand segmentation using airborne lidar data and very high resolution multispectral imagery / Clément Dechesne (2016)
Titre : Forest stand segmentation using airborne lidar data and very high resolution multispectral imagery Type de document : Article/Communication Auteurs : Clément Dechesne , Auteur ; Clément Mallet , Auteur ; Arnaud Le Bris , Auteur ; Valérie Gouet-Brunet , Auteur ; Alexandre Hervieu , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2016 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3 Projets : 1-Pas de projet / Conférence : ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3 Importance : pp 207 - 214 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme Graph-Cut
[Termes IGN] classification dirigée
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] semis de pointsRésumé : (auteur) Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., ≥ 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with α-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%). Numéro de notice : C2016-040 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : FORET/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLI-B3-207-2016 Date de publication en ligne : 09/06/2016 En ligne : https://doi.org/10.5194/isprs-archives-XLI-B3-207-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91852 Documents numériques
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