Centre de documentation
scientifique

Variable selection for estimating individual tree height using genetic algorithm and random forest / Evandro Nunes Miranda in Forest ecology and management, vol 504 (January-15 2022)  

Public [article]  inForest ecology and management > vol 504 (January-15 2022) . - n° 119828 Titre : Variable selection for estimating individual tree height using genetic algorithm and random forest Type de document : Article/Communication Auteurs : Evandro Nunes Miranda, Auteur ; Bruno Henrique Groenner Barbosa, Auteur ; Sergio Henrique Godinho Silva, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 119828 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique [Termes IGN] Brésil [Termes IGN] classification par algorithme génétique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] hauteur des arbres [Termes IGN] modélisation de la forêt [Termes IGN] optimisation (mathématiques) [Vedettes matières IGN] Foresterie Résumé : (auteur) Tree height is an important trait in forest science and is highly associated with the site quality from which the trees are measured. However, other factors, such as competition and species interaction, may yield better estimates for individual tree height when taken into account, but these variables have so far been challenging in model fitting. We propose a hybrid approach using genetic algorithms for variables selection and a machine learning algorithm (random forest) for fitting models of individual tree heights. We compare our proposed hybrid method with a mixed-effects model and random forest model using a dataset of 5,608 trees and 189 environmental variables (forest inventory-based variables, soil, topographic, climate, spectral, and geographic) from sites in southeastern Brazil. The tree height models were evaluated using the coefficient of determination, absolute bias, and root means square error (RMSE) based on the validation of dataset performance. The optimal set of variables of the proposed method include the ratio of diameter at breast height to quadratic mean diameter, distance independent competition index, dominant height, the soil silt and boron content. Our findings showed that the proposed hybrid method achieved an accuracy comparable with other methodologies in estimating the total height of the individual trees, and such a modelling approach could have broader applications in forestry and ecological science where a studied response trait has a large number of potential explanatory variables. Numéro de notice : A2022-021 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119828 Date de publication en ligne : 06/11/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119828 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99216 [article]

Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images / Zhenjiang Wu in Remote sensing, vol 13 n° 20 (October-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 20 (October-2 2021) . - n° 4067 Titre : Superpixel-based regional-scale grassland community classification using genetic programming with Sentinel-1 SAR and Sentinel-2 multispectral images Type de document : Article/Communication Auteurs : Zhenjiang Wu, Auteur ; Jiahua Zhang, Auteur ; Fan Deng, Auteur Année de publication : 2021 Article en page(s) : n° 4067 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] Chine [Termes IGN] classification par algorithme génétique [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] indice de végétation [Termes IGN] optimisation (mathématiques) [Termes IGN] prairie [Termes IGN] précision de la classification [Termes IGN] superpixel [Termes IGN] texture d'image Résumé : (auteur) Grasslands are one of the most important terrestrial ecosystems on the planet and have significant economic and ecological value. Accurate and rapid discrimination of grassland communities is critical to the conservation and utilization of grassland resources. Previous studies that explored grassland communities were mainly based on field surveys or airborne hyperspectral and high-resolution imagery. Limited by workload and cost, these methods are typically suitable for small areas. Spaceborne mid-resolution RS images (e.g., Sentinel, Landsat) have been widely used for large-scale vegetation observations owing to their large swath width. However, there still keep challenges in accurately distinguishing between different grassland communities using these images because of the strong spectral similarity of different communities and the suboptimal performance of models used for classification. To address this issue, this paper proposed a superpixel-based grassland community classification method using Genetic Programming (GP)-optimized classification model with Sentinel-2 multispectral bands, their derived vegetation indices (VIs) and textural features, and Sentinel-1 Synthetic Aperture Radar (SAR) bands and the derived textural features. The proposed method was evaluated in the Siziwang grassland of China. Our results showed that the addition of VIs and textures, as well as the use of GP-optimized classification models, can significantly contribute to distinguishing grassland communities, and the proposed approach classified the seven communities in Siziwang grassland with an overall accuracy of 84.21% and a kappa coefficient of 0.81. We concluded that the classification method proposed in this paper is capable of distinguishing grassland communities with high accuracy at a regional scale. Numéro de notice : A2021-805 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204067 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.3390/rs13204067 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98862 [article]

Missing-area reconstruction in multispectral images under a compressive sensing perspective / Luca Lorenzi in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3998 - 4008 Titre : Missing-area reconstruction in multispectral images under a compressive sensing perspective Type de document : Article/Communication Auteurs : Luca Lorenzi, Auteur ; Farid Melgani, Auteur ; Grégoire Mercier, Auteur Année de publication : 2013 Article en page(s) : pp 3998 - 4008 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par algorithme génétique [Termes IGN] équation linéaire [Termes IGN] image Formosat/COSMIC [Termes IGN] image SPOT 5 [Termes IGN] nébulosité [Termes IGN] nuage [Termes IGN] régression Résumé : (Auteur) The intent of this paper is to propose new methods for the reconstruction of areas obscured by clouds. They are based on compressive sensing (CS) theory, which allows finding sparse signal representations in underdetermined linear equation systems. In particular, two common CS solutions are adopted for our reconstruction problem: the basis pursuit and the orthogonal matching pursuit methods. A novel alternative CS solution is also proposed through a formulation within a multiobjective genetic optimization scheme. To illustrate the performances of the proposed methods, a thorough experimental analysis on FORMOsa SATellite-2 and Satellite Pour l'Observation de la Terre-5 multispectral images is reported and discussed. It includes a detailed simulation study that aims at assessing the accuracy of the methods in different qualitative and quantitative cloud-contamination conditions. Compared with state-of-the-art techniques for cloud removal, the proposed methods show a clear superiority, which makes them a promising tool in cleaning images in the presence of clouds. Numéro de notice : A2013-372 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227329 En ligne : https://doi.org/10.1109/TGRS.2012.2227329 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32510 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013071A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A genetic fuzzy-rule-based classifier for land cover classification from hyperspectral imagery / Dimitris G. Stavrakoudis in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 1 (January 2012) . - pp 130 - 148 Titre : A genetic fuzzy-rule-based classifier for land cover classification from hyperspectral imagery Type de document : Article/Communication Auteurs : Dimitris G. Stavrakoudis, Auteur ; G. Galidaki, Auteur ; Ioannis Z. Gitas, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 130 - 148 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par algorithme génétique [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] occupation du sol Résumé : (Auteur) This paper proposes the use of a genetic fuzzy-rule-based classification system for land cover classification from hyperspectral images. The proposed classifier, namely, Feature Selective Linguistic Classifier, is constructed through a three-stage learning process. The first stage produces a preliminary fuzzy rule base in an iterative fashion. During this stage, a local feature selection scheme is employed, designed to guide the genetic evolution, through the evaluation of deterministic information about the relevance of each feature with respect to its classification ability. The structure of the model is then simplified in a subsequent postprocessing stage. The performance of the classifier is finally optimized through a genetic tuning stage. An extensive comparative analysis, using an Earth Observing-1 Hyperion satellite image, highlights the quality advantages of the proposed system, when compared with nonfuzzy classifiers, commonly employed in hyperspectral classification tasks. Numéro de notice : A2012-032 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2159613 Date de publication en ligne : 29/07/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2159613 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31480 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Genetic algorithms for the calibration of cellular automata urban growth modeling / J. Shan in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 10 (October 2008) . - pp 1267 - 1277 Titre : Genetic algorithms for the calibration of cellular automata urban growth modeling Type de document : Article/Communication Auteurs : J. Shan, Auteur ; S. Alkheder, Auteur ; Jing Wang, Auteur Année de publication : 2008 Article en page(s) : pp 1267 - 1277 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] automate cellulaire [Termes IGN] classification par algorithme génétique [Termes IGN] croissance urbaine Résumé : (Auteur) This paper discusses the use of genetic algorithms to enhance the efficiency of transition rule calibration in cellular automata urban growth modeling. The cellular automata model is designed as a function of multitemporal satellite imagery and population density. Transition rules in the model identify the required neighborhood urbanization level for a test pixel to develop to urban. Calibration of the model is initially performed by exhaustive search, where the entire solution space is examined to find the best set of rule values. This method is computationally extensive and needs to consider all possible combinations for the transition rules. The rise in the number of variables will exponentially increase the time required for running and calibrating the model. This study introduces genetic algorithms as an effective solution to the calibration problem. It is shown that the genetic algorithms are able to produce modeling results close to the ones obtained from the exhaustive search in a time effective manner. Optimal rule values can be reached within the early generations of genetic algorithms. It is expected that genetic algorithms will significantly benefit urban modeling problems with larger set of input data and bigger solution spaces. Copyright ASPRS Numéro de notice : A2008-376 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.10.1267 En ligne : https://doi.org/10.14358/PERS.74.10.1267 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29369 [article]

Comparison of computational intelligence based classification techniques for remotely sensed optical image classification / D. Stathakis in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)  

Permalink