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See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning / Zhouxin Xi in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 168 (October 2020) . - pp 1 - 16 Titre : See the forest and the trees: Effective machine and deep learning algorithms for wood filtering and tree species classification from terrestrial laser scanning Type de document : Article/Communication Auteurs : Zhouxin Xi, Auteur ; Christopher Hopkinson, Auteur ; Stewart B. Rood, Auteur ; Derek R. Peddle, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] classification [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] espèce végétale [Termes IGN] gestion forestière [Termes IGN] semis de points [Termes IGN] variation saisonnière Résumé : (auteur) Determining tree species composition in natural forests is essential for effective forest management. Species classification at the individual tree level requires fine-scale traits which can be derived through terrestrial laser scanning (TLS) point clouds. A generalizable species classification framework also needs to decouple seasonal foliage variation from deciduous species, for which wood filtering is applicable. Different machine learning and deep learning models are feasible for wood filtering and species classification. We investigated 13 machine learning and deep learning classifiers for 9 species, and 15 classifiers for filtering wood points from TLS plot scans. Each classifier was evaluated using the criteria of mean Intersection over Union accuracy (mIoU), training stability and time cost. On average, deep learning classifiers outperformed machine learning classifiers by 10% and 5% in terms of wood and species classification mIoU, respectively. PointNet++ provided the best species classifier, with the highest mIoU (0.906), stability, and moderate time cost. Among wood classifiers, UNet achieved the top mIoU (0.839) while ResNet-50 was recommended for rapid trial and error testing. Across the classifications, the factors of input resolution, attributes and features were also analyzed. Hot zones of species classification with PointNet++ were visualized to indicate how AI interpret species traits. Numéro de notice : A2020-533 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.08.001 Date de publication en ligne : 10/08/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.08.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95718 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020101	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2020103	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2020102	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Improved topographic correction of forest image data using a 3D canopy reflectance model in multiple forward mode / S.A. Soenen in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 1007 - 1027 Titre : Improved topographic correction of forest image data using a 3D canopy reflectance model in multiple forward mode Type de document : Article/Communication Auteurs : S.A. Soenen, Auteur ; Derek R. Peddle, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 1007 - 1027 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Alberta (Canada) [Termes IGN] canopée [Termes IGN] classe d'objets [Termes IGN] correction du signal [Termes IGN] forêt tempérée [Termes IGN] image SPOT [Termes IGN] modélisation 3D [Termes IGN] Pinus (genre) [Termes IGN] précision de la classification [Termes IGN] réflectance végétale [Termes IGN] variance Résumé : (Auteur) In most forestry remote sensing applications in steep terrain, simple photometric and empirical (PE) topographic corrections are confounded as a result of stand structure and species assemblages that vary with terrain and the anisotropic reflective properties of vegetated surfaces. To address these problems, we present MFM-TOPO as a new physically-based modelling (PBM) approach for normalising topographically induced signal variance as a function of forest stand structure and sub-pixel scale components. MFM-TOPO uses the Li-Strahler geometric optical mutual shadowing (GOMS) canopy reflectance model in Multiple Forward Mode (MFM) to account for slope and aspect influences directly. MFM-TOPO has an explicit physical-basis and uses sun-canopy-sensor (SCS) geometry that is more appropriate than strictly terrain-based corrections in forested areas since it preserves the geotropic nature of trees (vertical growth with respect to the geoid) regardless of terrain, view and illumination angles. MFM-TOPO is compared against our recently developed SCS+C correction and a comprehensive set of other existing PE and SCS methods (cosine, C correction, Minnaert, statistical-empirical, SCS, and b correction) for removing topographically induced variance and for improving SPOT image classification accuracy in a Rocky Mountain forest in Kananaskis, Alberta Canada. MFM-TOPO removed the most terrain-based variance and provided the greatest improvement in classification accuracy within a species and stand density based class structure. For example, pine class accuracy was increased by 62% over shaded slopes, and spruce class accuracy was increased by 13% over more moderate slopes. In addition to classification, MFM-TOPO is suitable for retrieving biophysical parameters in mountainous terrain. Numéro de notice : A2008-007 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701311291 En ligne : https://doi.org/10.1080/01431160701311291 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29002 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Classification of Spot HRV imagery and texture features / Steven E. Franklin in International Journal of Remote Sensing IJRS, vol 11 n° 3 (March 1990)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 11 n° 3 (March 1990) . - pp 551 - 556 Titre : Classification of Spot HRV imagery and texture features Type de document : Article/Communication Auteurs : Steven E. Franklin, Auteur ; Derek R. Peddle, Auteur Année de publication : 1990 Article en page(s) : pp 551 - 556 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse texturale [Termes IGN] Canada [Termes IGN] classification [Termes IGN] image multibande [Termes IGN] image SPOT-HRV [Termes IGN] occupation du sol [Termes IGN] précision [Termes IGN] relief [Termes IGN] texture d'image Numéro de notice : A1990-068 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431169008955039 En ligne : https://doi.org/10.1080/01431169008955039 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=25486 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-90031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spectral texture for improved class discrimination in complex terrain / Steven E. Franklin in International Journal of Remote Sensing IJRS, vol 10 n° 8 (August 1989)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 10 n° 8 (August 1989) . - pp 1437 - 1443 Titre : Spectral texture for improved class discrimination in complex terrain Type de document : Article/Communication Auteurs : Steven E. Franklin, Auteur ; Derek R. Peddle, Auteur Année de publication : 1989 Article en page(s) : pp 1437 - 1443 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse texturale [Termes IGN] Canada [Termes IGN] classification [Termes IGN] géomorphologie locale [Termes IGN] image Landsat-MSS [Termes IGN] modèle numérique de surface [Termes IGN] photo-interprétation [Termes IGN] signature spectrale [Termes IGN] texture d'image Numéro de notice : A1989-230 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431168908903979 En ligne : https://doi.org/10.1080/01431168908903979 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=25190 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-89071	RAB	Revue	Centre de documentation	En réserve L003	Disponible