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A deep learning approach to DTM extraction from imagery using rule-based training labels / Caroline M. Gevaert in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 106 - 123 Titre : A deep learning approach to DTM extraction from imagery using rule-based training labels Type de document : Article/Communication Auteurs : Caroline M. Gevaert, Auteur ; Claudio Persello, Auteur ; M. George Vosselman, Auteur Année de publication : 2018 Article en page(s) : pp 106 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de règles [Termes descripteurs IGN] benchmark spatial [Termes descripteurs IGN] Dar-es-Salam (Tanzanie) [Termes descripteurs IGN] drone [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] Kigali (Rwanda) [Termes descripteurs IGN] Lombardie [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] photogrammétrie aérienne [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) Existing algorithms for Digital Terrain Model (DTM) extraction still face difficulties due to data outliers and geometric ambiguities in the scene such as contiguous off-ground areas or sloped environments. We postulate that in such challenging cases, the radiometric information contained in aerial imagery may be leveraged to distinguish between ground and off-ground objects. We propose a method for DTM extraction from imagery which first applies morphological filters to the Digital Surface Model to obtain candidate ground and off-ground training samples. These samples are used to train a Fully Convolutional Network (FCN) in the second step, which can then be used to identify ground samples for the entire dataset. The proposed method harnesses the power of state-of-the-art deep learning methods, while showing how they can be adapted to the application of DTM extraction by (i) automatically selecting and labelling dataset-specific samples which can be used to train the network, and (ii) adapting the network architecture to consider a larger surface area without unnecessarily increasing the computational burden. The method is successfully tested on four datasets, indicating that the automatic labelling strategy can achieve an accuracy which is comparable to the use of manually labelled training samples. Furthermore, we demonstrate that the proposed method outperforms two reference DTM extraction algorithms in challenging areas. Numéro de notice : A2018-298 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.06.001 date de publication en ligne : 15/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90410 [article]

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Digital aerial photogrammetry for assessing cumulative spruce budworm defoliation and enhancing forest inventories at a landscape-level / Tristan R.H. Goodbody in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 1 - 11 Titre : Digital aerial photogrammetry for assessing cumulative spruce budworm defoliation and enhancing forest inventories at a landscape-level Type de document : Article/Communication Auteurs : Tristan R.H. Goodbody, Auteur ; Nicholas C. Coops, Auteur ; Txomin Hermosilla, Auteur ; Piotr Tompalski, Auteur ; Grant MacCartney, Auteur ; David A. MacLean, Auteur Année de publication : 2018 Article en page(s) : pp 1 - 11 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] défoliation [Termes descripteurs IGN] dégradation de la flore [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] insecte nuisible [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] Ontario [Termes descripteurs IGN] photogrammétrie aérienne [Termes descripteurs IGN] photogrammétrie numérique [Termes descripteurs IGN] Picea abies [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] surface terrière [Termes descripteurs IGN] surveillance forestière [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Spruce budworm (Choristoneura fumiferana [Clem.], Lepidoptera: Tortricidae) is a native defoliating insect with an important disturbance role in the eastern boreal forests of North America. With an extensive history of outbreaks and associated impacts on forest structural changes and timber supply, the mapping of spruce budworm defoliation has been of major management importance. In this study we assessed the ability of high spatial resolution digital aerial photogrammetric (DAP) data to predict cumulative defoliation as well as basal area and merchantable volume in spruce budworm host stands in the Gordon Cosens Forest south of Kapuskasing, Ontario, Canada. To do so, DAP derived structural and spectral metrics were incorporated to implement a stratified sampling design to improve the efficiency and cost-effectiveness of field surveying. Standard forest inventory measurements including diameter and height, as well as ocular and branch level defoliation assessments were undertaken on thirty 400 m2 radius plots. A partial least squares analysis was performed to determine whether structural metrics from a DAP point cloud could be influenced by spruce budworm defoliation, as well as determine the relative effectiveness of spectral (e.g. mean NDVI) vs. structural (e.g. 90th percentile of height) metrics, or their combination, for predicting cumulative defoliation. Results indicated that spectral metrics were the most effective for predicting cumulative defoliation (R2 = 0.79), while structural metrics were the least effective (R2 = 0.49). Metrics characterizing variance of the spectral values were found to be the most important predictors. Structural metrics and linear regression were also used to estimate landscape-level volume and basal area per hectare yielding R2 = 0.80 and R2 = 0.90, respectively. Outcomes of this analysis indicate that DAP-derived spectral metrics were more capable of modeling cumulative defoliation, while structural metrics were effective for landscape-level estimations of standard forest inventory attributes. This analysis indicated that the provision of both spectral and structural metrics from a single aerial imagery survey has potential to enhance defoliation monitoring and forest attribute modeling at a landscape-level. Numéro de notice : A2018-290 Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.012 date de publication en ligne : 01/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90408 [article]

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Combined calibration method based on rational function model for the Chinese GF-1 wide-field-of-view imagery / Taoyang Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 4 (April 2016)

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 4 (April 2016) . - pp 291 - 298 Titre : Combined calibration method based on rational function model for the Chinese GF-1 wide-field-of-view imagery Type de document : Article/Communication Auteurs : Taoyang Wang, Auteur ; Guo Zhang, Auteur ; Yonghua Jiang, Auteur Année de publication : 2016 Article en page(s) : pp 291 - 298 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes descripteurs IGN] champ visuel [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] étalonnage de capteur (imagerie) [Termes descripteurs IGN] image GF-1 [Termes descripteurs IGN] modèle par fonctions rationnelles [Termes descripteurs IGN] orientation interne Résumé : (auteur) The internal geometric distortion of charge-coupled-device (CCD) chips degrades the internal geometric accuracy of satellite images. Even if a large number of ground control points (GCPs) are used for geometric correction, the satellite images cannot always be used for direct mapping. Because of the current lack of core parameters for building a rigorous geometric model, this paper proposes a combined interior-orientation calibration method based on the rational function model (RFM) for multiple GF-1 wide-field-of-view (WFV) images of the same general scene. A polynomial model of look angles in the image space is chosen to compensate for the systemic error of CCD chips. After calibration for sensors 1 to 4, the orientation accuracy is improved to 0.4 pixels. Moreover, the residual error figures show no obvious trend. Other GF-1 WFV images are calibrated using the obtained interior-orientation parameters, and the improvement in their orientation accuracy is demonstrated to validate the effectiveness of the proposed method. Numéro de notice : A2016-181 Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80524 [article]

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Sampling piecewise convex unmixing and endmember extraction / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 2 (March 2013)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 3 Tome 2 (March 2013) . - pp 1655 - 1665 Titre : Sampling piecewise convex unmixing and endmember extraction Type de document : Article/Communication Auteurs : Alina Zare, Auteur ; Paul Garder, Auteur ; George Casella, Auteur Année de publication : 2013 Article en page(s) : pp 1655 - 1665 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme du simplexe [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] ensemble convexe [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] signature spectrale Résumé : (Auteur) A Metropolis-within-Gibbs sampler for piecewise convex hyperspectral unmixing and endmember extraction is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data are often nonconvex. Furthermore, in standard endmember extraction and unmixing methods, endmembers are generally represented as a single point in the high-dimensional space. However, the spectral signature for a material varies as a function of the inherent variability of the material and environmental conditions. Therefore, it is more appropriate to represent each endmember as a full distribution and use this information during spectral unmixing. The proposed method searches for several sets of endmember distributions. By using several sets of endmember distributions, a piecewise convex mixing model is applied, and given this model, the proposed method performs spectral unmixing and endmember estimation given this nonlinear representation of the data. Each set represents a random simplex. The vertices of the random simplex are modeled by the endmember distributions. The hyperspectral data are partitioned into sets associated with each of the extracted sets of endmember distributions using a Dirichlet process prior. The Dirichlet process prior also estimates the number of sets. Thus, the Metropolis-within-Gibbs sampler partitions the data into convex regions, estimates the required number of convex regions, and estimates endmember distributions and abundance values for all convex regions. Results are presented on real hyperspectral and simulated data that indicate the ability of the method to effectively estimate endmember distributions and the number of sets of endmember distributions. Numéro de notice : A2013-134 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32272 [article]

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A framework for supervised image classification with incomplete training samples / Q. Guo in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 6 (June 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 6 (June 2012) . - pp 595 - 604 Titre : A framework for supervised image classification with incomplete training samples Type de document : Article/Communication Auteurs : Q. Guo, Auteur ; W. Li, Auteur ; J. Chen, Auteur Année de publication : 2012 Article en page(s) : pp 595 - 604 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] échantillonnage multiple [Termes descripteurs IGN] extraction de couche Résumé : (Auteur) For traditional supervised classification methods, all land-cover types need to be exhaustively labeled to train the classifier. However, there are situations where the training sample classes are incomplete due to a lack of understanding of ground cover types in the image. In this study we propose a one-by-one (OBO) classification framework to address this incomplete training sample problem. The OBO approach is based on a one-class classifier (positive and unlabeled learning algorithm), and it extracts the land-cover type from the image one at a time. The performance of the proposed method was compared with a traditional supervised classifier using a high spatial resolution image. The average accuracy of the new method is 76.34 percent across different training sample sizes, whereas the accuracy of the classical approach is 66.46 percent, with an increase of 9.88 percent. The results demonstrate that the proposed new framework provides significantly higher classification accuracy than the classical approach at the 95 percent confidence level, and shows promise in dealing with the incomplete training sample problem for supervised image classification. Numéro de notice : A2012-249 Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.78.6.595 En ligne : https://doi.org/10.14358/PERS.78.6.595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31695 [article]

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Persistent scatterer interferometry : potential, limits and initial C- and X-band comparison / M. Crosetto in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 9 (September 2010)  

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Quantifiying the building stock optical high-resolution satellite imagery for assessing disaster risk / D. Ehrlich in Geocarto international, vol 25 n° 4 (July 2010)  

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Sampling approaches for one-pass land-use/land-cover change mapping / Z. Huang in International Journal of Remote Sensing IJRS, vol 31 n° 6 (March 2010)

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Observations of urban and suburban environments with global satellite scatterometer data / Son V. Nghiem in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)

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The influence of thematic and spatial resolution on maps of a coral reef ecosystem / M. Kendall in Marine geodesy, vol 31 n° 2 (June - September 2008)

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Spatial sampling uncertainty in SMEX04 soil moisture fields: a data-based resampling experiment / M. Gebremichael in Remote sensing of environment, vol 112 n° 2 (15/02/2008)

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Imagerie spatiale / P. Lier (2008)

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Introduction au traitement d'images / D. Lingrand (2008)

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DEM resolution dependencies of terrain attributes across a landscape / Y. Deng in International journal of geographical information science IJGIS, vol 21 n° 1-2 (january 2007)

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Angular resolution of terrestrial laser scanners / Derek D. Lichti in Photogrammetric record, vol 21 n° 114 (June - August 2006)

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