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Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning / Aboubakar Sani-Mohammed in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 6 (December 2022)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 6 (December 2022) . - n° 100024 Titre : Instance segmentation of standing dead trees in dense forest from aerial imagery using deep learning Type de document : Article/Communication Auteurs : Aboubakar Sani-Mohammed, Auteur ; Wei Yao, Auteur ; Marco Heurich, Auteur Année de publication : 2022 Article en page(s) : n° 100024 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] arbre mort [Termes IGN] Bavière (Allemagne) [Termes IGN] bois sur pied [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection automatique [Termes IGN] gestion forestière durable [Termes IGN] image à haute résolution [Termes IGN] image aérienne [Termes IGN] image infrarouge couleur [Termes IGN] peuplement mélangé [Termes IGN] puits de carbone [Termes IGN] segmentation sémantique Résumé : (auteur) Mapping standing dead trees, especially, in natural forests is very important for evaluation of the forest's health status, and its capability for storing Carbon, and the conservation of biodiversity. Apparently, natural forests have larger areas which renders the classical field surveying method very challenging, time-consuming, labor-intensive, and unsustainable. Thus, for effective forest management, there is the need for an automated approach that would be cost-effective. With the advent of Machine Learning, Deep Learning has proven to successfully achieve excellent results. This study presents an adjusted Mask R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed dense forest from CIR aerial imagery using a limited (195 images) training dataset. First, transfer learning is considered coupled with the image augmentation technique to leverage the limitation of training datasets. Then, we strategically selected hyperparameters to suit appropriately our model's architecture that fits well with our type of data (dead trees in images). Finally, to assess the generalization capability of our model's performance, a test dataset that was not confronted to the deep neural network was used for comprehensive evaluation. Our model recorded promising results reaching a mean average precision, average recall, and average F1-Score of 0.85, 0.88, and 0.87 respectively, despite our relatively low resolution (20 cm) dataset. Consequently, our model could be used for automation in standing dead tree detection and segmentation for enhanced forest management. This is equally significant for biodiversity conservation, and forest Carbon storage estimation. Numéro de notice : A2022-871 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100024 Date de publication en ligne : 10/11/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100024 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102165 [article]

Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets / Yusheng Xu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 106 - 123 Titre : Pairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets Type de document : Article/Communication Auteurs : Yusheng Xu, Auteur ; Richard Boerner, Auteur ; Wei Yao, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 106 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] appariement d'images [Termes IGN] congruence [Termes IGN] données 4D [Termes IGN] données lidar [Termes IGN] données spatiotemporelles [Termes IGN] modèle stéréoscopique [Termes IGN] octree [Termes IGN] Ransac (algorithme) [Termes IGN] scène urbaine [Termes IGN] semis de points [Termes IGN] surface plane [Termes IGN] voxel Résumé : (Auteur) To ensure complete coverage when measuring a large-scale urban area, pairwise registration between point clouds acquired via terrestrial laser scanning or stereo image matching is usually necessary when there is insufficient georeferencing information from additional GNSS and INS sensors. In this paper, we propose a semi-automatic and target-less method for coarse registration of point clouds using geometric constraints of voxel-based 4-plane congruent sets (V4PCS). The planar patches are firstly extracted from voxelized point clouds. Then, the transformation invariant, 4-plane congruent sets are constructed from extracted planar surfaces in each point cloud. Initial transformation parameters between point clouds are estimated via corresponding congruent sets having the highest registration scores in the RANSAC process. Finally, a closed-form solution is performed to achieve optimized transformation parameters by finding all corresponding planar patches using the initial transformation parameters. Experimental results reveal that our proposed method can be effective for registering point clouds acquired from various scenes. A success rate of better than 80% was achieved, with average rotation errors of about 0.5 degrees and average translation errors less than approximately 0.6 m. In addition, our proposed method is more efficient than other baseline methods when using the same hardware and software configuration conditions. Numéro de notice : A2019-207 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.02.015 Date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.02.015 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92673 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019053	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019052	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Detection of fallen trees in ALS point clouds using a Normalized Cut approach trained by simulation / Przemyslaw Polewski in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 105 (July 2015) . - pp 252 - 271 Titre : Detection of fallen trees in ALS point clouds using a Normalized Cut approach trained by simulation Type de document : Article/Communication Auteurs : Przemyslaw Polewski, Auteur ; Wei Yao, Auteur ; Marco Heurich, Auteur ; Peter Krzystek, Auteur ; Uwe Stilla, Auteur Année de publication : 2015 Article en page(s) : pp 252 - 271 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre mort [Termes IGN] Bavière (Allemagne) [Termes IGN] détection automatique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire forestier local [Termes IGN] parc naturel national [Termes IGN] semis de points Résumé : (auteur) Downed dead wood is regarded as an important part of forest ecosystems from an ecological perspective, which drives the need for investigating its spatial distribution. Based on several studies, Airborne Laser Scanning (ALS) has proven to be a valuable remote sensing technique for obtaining such information. This paper describes a unified approach to the detection of fallen trees from ALS point clouds based on merging short segments into whole stems using the Normalized Cut algorithm. We introduce a new method of defining the segment similarity function for the clustering procedure, where the attribute weights are learned from labeled data. Based on a relationship between Normalized Cut’s similarity function and a class of regression models, we show how to learn the similarity function by training a classifier. Furthermore, we propose using an appearance-based stopping criterion for the graph cut algorithm as an alternative to the standard Normalized Cut threshold approach. We set up a virtual fallen tree generation scheme to simulate complex forest scenarios with multiple overlapping fallen stems. This simulated data is then used as a basis to learn both the similarity function and the stopping criterion for Normalized Cut. We evaluate our approach on 5 plots from the strictly protected mixed mountain forest within the Bavarian Forest National Park using reference data obtained via a manual field inventory. The experimental results show that our method is able to detect up to 90% of fallen stems in plots having 30–40% overstory cover with a correctness exceeding 80%, even in quite complex forest scenes. Moreover, the performance for feature weights trained on simulated data is competitive with the case when the weights are calculated using a grid search on the test data, which indicates that the learned similarity function and stopping criterion can generalize well on new plots. Numéro de notice : A2015-703 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.01.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.01.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78339 [article]