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Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR / Denis Horvat in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 1 – 14 Titre : Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR Type de document : Article/Communication Auteurs : Denis Horvat, Auteur ; Borut Žalik, Auteur ; Domen Mongus, Auteur Année de publication : 2016 Article en page(s) : pp 1 – 14 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de sensibilité [Termes IGN] classification dirigée [Termes IGN] détection automatique [Termes IGN] distribution spatiale [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] méthode robuste [Termes IGN] morphologie mathématique [Termes IGN] prise en compte du contexte [Termes IGN] végétation [Termes IGN] zone rurale Résumé : (auteur) This paper proposes a new method for the detection of vegetation in LiDAR data. As vegetation points are characterised by non-linear distributions, they are efficiently recognised based-on large errors obtained when applying the local fitting of planar surfaces. In addition, three contextual filters are introduced capable of dealing with those exceptions that do not conform with previous interpretations. Namely, they are designed for detecting overgrowing vegetation, small objects attached to the planar surfaces (such as balconies, chimneys, and noise within the buildings) and small objects that do not belong to vegetation (vehicles, statues, fences). During the validation, the proposed method achieved over 97% correctness as well as completeness of vegetation recognition in rural areas while its average accuracy in urban settings was 90.7% in terms of F1F1-scores. The method uses only three input parameters and allows for efficient compensation between completeness and correctness, without significantly affecting the F1F1-score. Sensitivity analysis of the method also confirmed the robustness against a sub-optimal definition of the input parameters. Numéro de notice : A2016-576 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.02.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.02.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81706 [article]

The variants of an LOD of a 3D building model and their influence on spatial analyses / Filip Biljecki in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 42 – 54 Titre : The variants of an LOD of a 3D building model and their influence on spatial analyses Type de document : Article/Communication Auteurs : Filip Biljecki, Auteur ; Hugo Ledoux, Auteur ; Jantien E. Stoter, Auteur ; M. George Vosselman, Auteur Année de publication : 2016 Article en page(s) : pp 42 – 54 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse spatiale [Termes IGN] CityGML [Termes IGN] cohérence géométrique [Termes IGN] méthode de Monte-Carlo [Termes IGN] niveau de détail [Termes IGN] reconstruction 3D du bâti [Termes IGN] SIG 3D Résumé : (auteur) The level of detail (LOD) of a 3D city model indicates the model’s grade and usability. However, there exist multiple valid variants of each LOD. As a consequence, the LOD concept is inconclusive as an instruction for the acquisition of 3D city models. For instance, the top surface of an LOD1 block model may be modelled at the eaves of a building or at its ridge height. Such variants, which we term geometric references, are often overlooked and are usually not documented in the metadata. Furthermore, the influence of a particular geometric reference on the performance of a spatial analysis is not known. In response to this research gap, we investigate a variety of LOD1 and LOD2 geometric references that are commonly employed, and perform numerical experiments to investigate their relative difference when used as input for different spatial analyses. We consider three use cases (estimation of the area of the building envelope, building volume, and shadows cast by buildings), and compute the deviations in a Monte Carlo simulation. The experiments, carried out with procedurally generated models, indicate that two 3D models representing the same building at the same LOD, but modelled according to different geometric references, may yield substantially different results when used in a spatial analysis. The outcome of our experiments also suggests that the geometric reference may have a bigger influence than the LOD, since an LOD1 with a specific geometric reference may yield a more accurate result than when using LOD2 models. Numéro de notice : A2016-577 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.03.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81709 [article]

Optical remotely sensed time series data for land cover classification: A review / Cristina Gómez in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 55 – 72 Titre : Optical remotely sensed time series data for land cover classification: A review Type de document : Article/Communication Auteurs : Cristina Gómez, Auteur ; Joanne C. White, Auteur ; Michael A. Wulder, Auteur Année de publication : 2016 Article en page(s) : pp 55 – 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification automatique [Termes IGN] détection de changement [Termes IGN] image Landsat [Termes IGN] image Sentinel-MSI [Termes IGN] occupation du sol [Termes IGN] série temporelle [Termes IGN] surveillance agricole [Termes IGN] traitement d'image Résumé : (auteur) Accurate land cover information is required for science, monitoring, and reporting. Land cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring and mapping of land cover and land cover change in a consistent and robust manner over large areas is made possible with Earth Observation (EO) data. Land cover products satisfying a range of science and policy information needs are currently produced periodically at different spatial and temporal scales. The increased availability of EO data—particularly from the Landsat archive (and soon to be augmented with Sentinel-2 data)—coupled with improved computing and storage capacity with novel image compositing approaches, have resulted in the availability of annual, large-area, gap-free, surface reflectance data products. In turn, these data products support the development of annual land cover products that can be both informed and constrained by change detection outputs. The inclusion of time series change in the land cover mapping process provides information on class stability and informs on logical class transitions (both temporally and categorically). In this review, we present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization. Numéro de notice : A2016-578 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.008 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.03.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81716 [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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 73 – 85 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 données Résumé : (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 [article]