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Mapping dead forest cover using a deep convolutional neural network and digital aerial photography / Jean-Daniel Sylvain in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 14 - 26 Titre : Mapping dead forest cover using a deep convolutional neural network and digital aerial photography Type de document : Article/Communication Auteurs : Jean-Daniel Sylvain, Auteur ; Guillaume Drolet, Auteur ; Nicolas Brown, Auteur Année de publication : 2019 Article en page(s) : pp 14 - 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre mort [Termes descripteurs IGN] couvert forestier [Termes descripteurs IGN] forêt boréale [Termes descripteurs IGN] peuplement mélangé [Termes descripteurs IGN] photographie aérienne [Termes descripteurs IGN] Québec (Canada) [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] santé des forêts Résumé : (Auteur) Tree mortality is an important forest ecosystem variable having uses in many applications such as forest health assessment, modelling stand dynamics and productivity, or planning wood harvesting operations. Because tree mortality is a spatially and temporally erratic process, rates and spatial patterns of tree mortality are difficult to estimate with traditional inventory methods. Remote sensing imagery has the potential to detect tree mortality at spatial scales required for accurately characterizing this process (e.g., landscape, region). Many efforts have been made in this sense, mostly using pixel- or object-based methods. In this study, we explored the potential of deep Convolutional Neural Networks (CNNs) to detect and map tree health status and functional type over entire regions. To do this, we built a database of around 290,000 photo-interpreted trees that served to extract and label image windows from 20 cm-resolution digital aerial images, for use in CNN training and evaluation. In this process, we also evaluated the effect of window size and spectral channel selection on classification accuracy, and we assessed if multiple realizations of a CNN, generated using different weight initializations, can be aggregated to provide more robust predictions. Finally, we extended our model with 5 additional classes to account for the diversity of landcovers found in our study area. When predicting tree health status only (live or dead), we obtained test accuracies of up to 94%, and up to 86% when predicting functional type only (broadleaf or needleleaf). Channel selection had a limited impact on overall classification accuracy, while window size increased the ability of the CNNs to predict plant functional type. The aggregation of multiple realizations of a CNN allowed us to avoid the selection of suboptimal models and help to remove much of the speckle effect when predicting on new aerial images. Test accuracies of plant functional type and health status were not affected in the extended model and were all above 95% for the 5 extra classes. Our results demonstrate the robustness of the CNN for between-scene variations in aerial photography and also suggest that this approach can be applied at operational level to map tree mortality across extensive territories. Numéro de notice : A2019-316 Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.010 date de publication en ligne : 02/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93353 [article]

Multi-sensor prediction of Eucalyptus stand volume: A support vector approach / Guilherme Silverio Aquino de Souza in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 135 - 146 Titre : Multi-sensor prediction of Eucalyptus stand volume: A support vector approach Type de document : Article/Communication Auteurs : Guilherme Silverio Aquino de Souza, Auteur ; Vicente Paulo Soares, Auteur ; Helio Garcia Leite, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 135 - 146 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bande L [Termes descripteurs IGN] Brésil [Termes descripteurs IGN] Eucalyptus (genre) [Termes descripteurs IGN] image ALOS-AVNIR2 [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] radar à antenne synthétique [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] taux d'échantillonnage Résumé : (Auteur) Stem volume is a key attribute of Eucalyptus forest plantations upon which decision-making is based at diverse levels of planning. Quantifying volume through remote sensing can support a proper management of forests. Because of limitations on spaceborne optical and synthetic aperture radar sensors, this study integrated both types of datasets assembled using support vector regression (SVR) to retrieve the stand volume of Eucalyptus plantations. We assessed different combinations of sensors and a minimum number of plots to develop an SVR model. Finally, the best SVR performance was compared with other analytical methods already tested and in the literature: multilinear regression, artificial neural networks (ANN), and random forest (RF). Here, we introduce a test for comparative analysis of the performance of different methods. We found that SVR accurately predicted stem volume of Brazilian fast-growing Eucalyptus forest plantations. Gaussian radial basis was the most suitable kernel function. Integrating the optical and L-band backscatter data increased the predictive accuracy compared to a single sensor model. Combining NIR-band data from ALOS AVNIR-2 and backscatter of L-band horizontal emitted and vertical received (HV) electric fields from ALOS PALSAR produced the most accurate SVR model (with an R2 of 0.926 and root mean square error of 11.007 m3/ha). The number of field plots sufficient for model development with non-redundant explanatory variables was 77. Under this condition, SVR performed similarly to ANN and outperformed the multiple linear regression and random forest methods. Numéro de notice : A2019-319 Thématique : FORET/IMAGERIE Nature : Article DOI : doi.org/10.1016/j.isprsjprs.2019.08.002 date de publication en ligne : 20/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93357 [article]

Addressing overfitting on point cloud classification using Atrous XCRF / Hasan Asy’ari Arief in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 90 - 101 Titre : Addressing overfitting on point cloud classification using Atrous XCRF Type de document : Article/Communication Auteurs : Hasan Asy’ari Arief, Auteur ; Ulf Geir Indahl, Auteur ; Geir-Harald Strand, Auteur ; Håvard Tveite, Auteur Année de publication : 2019 Article en page(s) : pp 90 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] champ aléatoire conditionnel [Termes descripteurs IGN] classification automatique [Termes descripteurs IGN] réseau de neurones profond [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] semis de points Résumé : (Auteur) Advances in techniques for automated classification of point cloud data introduce great opportunities for many new and existing applications. However, with a limited number of labelled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 85.0% in term of overall accuracy, and 71.1% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score. Additionally, transfer learning using the Bergen 2018 dataset, without model retraining, was also performed. Even though our proposal provides a consistent 3% improvement in term of accuracy, more work still needs to be done to alleviate the generalization problem on the domain adaptation and the transfer learning field. Numéro de notice : A2019-312 Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.002 date de publication en ligne : 11/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93337 [article]

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PPD: Pyramid Patch Descriptor via convolutional neural network / Jie Wan in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 9 (September 2019) . - pp 673 - 686 Titre : PPD: Pyramid Patch Descriptor via convolutional neural network Type de document : Article/Communication Auteurs : Jie Wan, Auteur ; Alper Yilmaz, Auteur ; Lei Yan, Auteur Année de publication : 2019 Article en page(s) : pp 673 - 686 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] benchmark spatial [Termes descripteurs IGN] données localisées de référence [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] état de l'art [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image satellite [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) Local features play an important role in remote sensing image matching, and handcrafted features have been excessively used in this area for a long time. This article proposes a pyramid convolutional neural triplet network that extracts a 128-dimensional deep descriptor that significantly improves the matching performance. The proposed approach first extracts deep descriptors of the anchor patches and corresponding positive patches in a batch using the proposed pyramid convolutional neural network. Following this step, the approaches chooses the closest negative patch for each anchor patch and corresponding positive patch pair to form the triplet sample based on the descriptor distances among all other image patches in the batch. These triplets are used to optimize the parameters of the network using a new loss function. We evaluated the proposed deep descriptors on two benchmark data sets (Brown and HPatches) as well as real image data sets. The results reveal that the proposed descriptor achieves the state-of-the-art performance on the Brown data set and a comparatively very high performance on the HPatches data set. The proposed approach finds more correct matches than the classical handcrafted feature descriptors on aerial image pairs and is observed to be robust to variations in the viewpoint and illumination. Numéro de notice : A2019-416 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.9.673 date de publication en ligne : 01/09/2019 En ligne : https://doi.org/10.14358/PERS.85.9.673 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93543 [article]

Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 151 - 162 Titre : Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network Type de document : Article/Communication Auteurs : Chunping Qiu, Auteur ; Lichao Mou, Auteur ; Michael Schmitt, Auteur ; Xiao Xiang Zhu, Auteur Année de publication : 2019 Article en page(s) : pp 151 - 162 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] climat urbain [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] réseau neuronal récurrent [Termes descripteurs IGN] résidu [Termes descripteurs IGN] ville Résumé : (Auteur) The local climate zone (LCZ) scheme was originally proposed to provide an interdisciplinary taxonomy for urban heat island (UHI) studies. In recent years, the scheme has also become a starting point for the development of higher-level products, as the LCZ classes can help provide a generalized understanding of urban structures and land uses. LCZ mapping can therefore theoretically aid in fostering a better understanding of spatio-temporal dynamics of cities on a global scale. However, reliable LCZ maps are not yet available globally. As a first step toward automatic LCZ mapping, this work focuses on LCZ-derived land cover classification, using multi-seasonal Sentinel-2 images. We propose a recurrent residual network (Re-ResNet) architecture that is capable of learning a joint spectral-spatial-temporal feature representation within a unitized framework. To this end, a residual convolutional neural network (ResNet) and a recurrent neural network (RNN) are combined into one end-to-end architecture. The ResNet is able to learn rich spectral-spatial feature representations from single-seasonal imagery, while the RNN can effectively analyze temporal dependencies of multi-seasonal imagery. Cross validations were carried out on a diverse dataset covering seven distinct European cities, and a quantitative analysis of the experimental results revealed that the combined use of the multi-temporal information and Re-ResNet results in an improvement of approximately 7 percent points in overall accuracy. The proposed framework has the potential to produce consistent-quality urban land cover and LCZ maps on a large scale, to support scientific progress in fields such as urban geography and urban climatology. Numéro de notice : A2019-268 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.004 date de publication en ligne : 14/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.004 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93085 [article]

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Sea level prediction in the Yellow Sea from satellite altimetry with a combined least squares-neural network approach / Jian Zhao in Marine geodesy, vol 42 n° 4 (July 2019)  

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Using direct transformation approach as an alternative technique to fuse global digital elevation models with GPS/levelling measurements in Egypt / Hossam Talaat Elshambaky in Journal of applied geodesy, vol 13 n° 3 (July 2019)  

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Using LiDAR-modified topographic wetness index, terrain attributes with leaf area index to improve a single-tree growth model in south-eastern Finland / Cheikh Mohamedou in Forestry, an international journal of forest research, vol 92 n° 3 (July 2019)  

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CNN-based dense image matching for aerial remote sensing images / Shunping Ji in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

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Semantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

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Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network / Jianfeng Huang in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images / Debaditya Acharya in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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BIM, SIG et recherche dans le secteur privé / Anonyme in Géomatique expert, n° 127 (avril - mai 2019)

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Journées de la recherche 2019 / Anonyme in Géomatique expert, n° 127 (avril - mai 2019)

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Learning high-level features by fusing multi-view representation of MLS point clouds for 3D object recognition in road environments / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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