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Toward optimum fusion of thermal hyperspectral and visible images in classification of urban area / Farhad Samadzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)
[article]
Titre : Toward optimum fusion of thermal hyperspectral and visible images in classification of urban area Type de document : Article/Communication Auteurs : Farhad Samadzadegan, Auteur ; Hadiseh Hasani, Auteur ; Peter Reinartz, Auteur Année de publication : 2017 Article en page(s) : pp 269 - 280 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bande visible
[Termes IGN] bati
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion d'images
[Termes IGN] géostatistique
[Termes IGN] image hyperspectrale
[Termes IGN] image thermique
[Termes IGN] indice de végétation
[Termes IGN] morphologie
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau routier
[Termes IGN] zone urbaineRésumé : (Auteur) Recently, classification of urban area based on multi-sensor fusion has been widely investigated. In this paper, the potential of using visible (VIS) and thermal infrared (TIR) hyperspectral images fusion for classification of urban area is evaluated. For this purpose, comprehensive spatial-spectral feature space is generated which includes vegetation index, differential morphological profile (DMP), attribute profile (AP), texture, geostatistical features, structural feature set (SFS) and local statistical descriptors from both datasets in addition to original datasets. Although Support Vector Machine (SVM) is an appropriate tool in the classification of high dimensional feature space, its performance is significantly affected by its parameters and feature space. Cuckoo search (CS) optimization algorithm with mixed binary-continuous coding is proposed for feature selection and SVM parameter determination simultaneously. Moreover, the significance of each selected feature category in the classification of a specific object is verified. Accuracy assessment on two subsets shows that stacking of VIS and TIR bands can improve the classification performance to 87 percent and 82 percent for two subsets, compare to VIS image (72 percent and 80 percent) and TIR image (50 percent and 56 percent). However, the optimum results obtained based on the proposed method which gains 94 percent and 92 percent. Furthermore, results show that using TIR beside VIS image improves classification accuracy of roads and buildings in urban area. Numéro de notice : A2017-111 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.4.269 En ligne : https://doi.org/10.14358/PERS.83.4.269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84589
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 4 (April 2017) . - pp 269 - 280[article]Constrained clustering by constraint programming / Thi-Bich-Hanh Dao in Artificial intelligence, vol 244 (March 2017)
[article]
Titre : Constrained clustering by constraint programming Type de document : Article/Communication Auteurs : Thi-Bich-Hanh Dao, Auteur ; Khanh-Chuong Duong, Auteur ; Christel Vrain, Auteur Année de publication : 2017 Article en page(s) : pp 70 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse de groupement
[Termes IGN] filtrage d'information
[Termes IGN] modélisation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] programmation par contraintesRésumé : (auteur) Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds of constraints, they are usually based on declarative frameworks and they are often exact methods, which either enumerate all the solutions satisfying the user constraints, or find a global optimum when an optimization criterion is specified. In a previous work, we have proposed a model for Constrained Clustering based on a Constraint Programming framework. It is declarative, allowing a user to integrate user constraints and to choose an optimization criterion among several ones. In this article we present a new and substantially improved model for Constrained Clustering, still based on a Constraint Programming framework. It differs from our earlier model in the way partitions are represented by means of variables and constraints. It is also more flexible since the number of clusters does not need to be set beforehand; only a lower and an upper bound on the number of clusters have to be provided. In order to make the model-based approach more efficient, we propose new global optimization constraints with dedicated filtering algorithms. We show that such a framework can easily be embedded in a more general process and we illustrate this on the problem of finding the optimal Pareto front of a bi-criterion constrained clustering task. We compare our approach with existing exact approaches, based either on a branch-and-bound approach or on graph coloring on twelve datasets. Experiments show that the model outperforms exact approaches in most cases. Numéro de notice : A2017-566 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.artint.2015.05.006 En ligne : https://doi.org/10.1016/j.artint.2015.05.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86680
in Artificial intelligence > vol 244 (March 2017) . - pp 70 - 94[article]Determining the appropriate timing of the next forest inventory: incorporating forest owner risk preferences and the uncertainty of forest data quality / Kyle J. Eyvindson in Annals of Forest Science, vol 74 n° 1 (March 2017)
[article]
Titre : Determining the appropriate timing of the next forest inventory: incorporating forest owner risk preferences and the uncertainty of forest data quality Type de document : Article/Communication Auteurs : Kyle J. Eyvindson, Auteur ; Aaron D. Petty, Auteur ; Annika S. Kangas, Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] aide à la décision
[Termes IGN] incertitude des données
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] programmation stochastique
[Termes IGN] risque naturel
[Termes IGN] simulation numérique
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) The timing to conduct new forest inventories should be based on the requirements of the decision maker. Importance should be placed on the objectives of the decision maker and his/her risk preferences related to those objectives.
Context : The appropriate use of pertinent and available information is paramount in any decision-making process. Within forestry, a new forest inventory is typically conducted prior to creating a forest management plan. The acquisition of new forest inventory data is justified by the simple statement of “good decisions require good data.”
Aims : By integrating potential risk preferences, we examine the specific needs to collect new forest information.
Methods : Through a two-stage stochastic programming with recourse model, we evaluate the specific timing to conduct a holding level forest inventory. A Monte Carlo simulation was used to integrate both inventory and growth model errors, resulting in a large number of potential scenarios process to be used as data for the stochastic program. To allow for recourse, an algorithm to sort the simulations to represent possible updated forest inventories, using the same data was developed.
Results : Risk neutral decision makers should delay obtaining new forest information when compared to risk averse decision makers.
Conclusion : New inventory data may only need to be collected rather infrequently; however, the exact timing depends on the forest owner’s objectives and risk preferences.Numéro de notice : A2017-042 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-016-0607-9 Date de publication en ligne : 08/02/2017 En ligne : https://doi.org/10.1007/s13595-016-0607-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84200
in Annals of Forest Science > vol 74 n° 1 (March 2017)[article]A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model / Rachel Whitsed in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)
[article]
Titre : A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model Type de document : Article/Communication Auteurs : Rachel Whitsed, Auteur ; Lisa T. Smallbone, Auteur Année de publication : 2017 Article en page(s) : pp 717 - 737 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme génétique
[Termes IGN] arbre (flore)
[Termes IGN] automate cellulaire
[Termes IGN] croissance des arbres
[Termes IGN] dynamique de la végétation
[Termes IGN] étalonnage des données
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle de croissance végétale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] sous-bois
[Termes IGN] Victoria (Australie)
[Vedettes matières IGN] ForesterieRésumé : (Auteur) Cellular automata (CA) models are commonly used to model vegetation dynamics, with the genetic algorithm (GA) being one method of calibration. This article investigates different GA settings, as well as the combination of a GA with a local optimiser to improve the calibration effort. The case study is a pattern-calibrated CA to model vegetation regrowth in central Victoria, Australia. We tested 16 GA models, varying population size, mutation rate, and level of allowable mutation. We also investigated the effect of applying a local optimiser, the Nelder‒Mead Downhill Simplex (NMDS) at GA convergence. We found that using a decreasing mutation rate can reduce computational cost while avoiding premature GA convergence, while increasing population size does not make the GA more efficient. The hybrid GA-NMDS can also reduce computational cost compared to a GA alone, while also improving the calibration metric. We conclude that careful consideration of GA settings, including population size and mutation rate, and in particular the addition of a local optimiser, can positively impact the efficiency and success of the GA algorithm, which can in turn lead to improved simulations using a well-calibrated CA model. Numéro de notice : A2017-081 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1231315 En ligne : http://dx.doi.org/10.1080/13658816.2016.1231315 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84344
in International journal of geographical information science IJGIS > vol 31 n° 3-4 (March-April 2017) . - pp 717 - 737[article]Réservation
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Titre : Semi-parametric segmentation of multiple series using a DP-Lasso strategy Type de document : Article/Communication Auteurs : Karine Bertin, Auteur ; Xavier Collilieux , Auteur ; Emilie Lebarbier, Auteur ; Christian Meza, Auteur Année de publication : 2017 Projets : 3-projet - voir note / Article en page(s) : pp 1255 - 1268 Note générale : bibliographie
This work was supported by FONDECYT [grant numbers 1141256 and 1141258]; ANILLO [grant number ACT-1112]; MATH-AmSud [grant number 16-MATH-03]; SIDRE and CONICYT [grant number 870100003].Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie
[Termes IGN] données géodésiques
[Termes IGN] estimation statistique
[Termes IGN] itération
[Termes IGN] programmation dynamique
[Termes IGN] segmentationRésumé : (auteur) We consider a semi-parametric approach to perform the joint segmentation of multiple series sharing a common functional part. We propose an iterative procedure based on Dynamic Programming for the segmentation part and Lasso estimators for the functional part. Our Lasso procedure, based on the dictionary approach, allows us to both estimate smooth functions and functions with local irregularity, which permits more flexibility than previous proposed methods. This yields to a better estimation of the functional part and improvements in the segmentation. The performance of our method is assessed using simulated data and real data from agriculture and geodetic studies. Our estimation procedure results to be a reliable tool to detect changes and to obtain an interpretable estimation of the functional part of the model in terms of known functions. Numéro de notice : A2017-870 Affiliation des auteurs : LASTIG LAREG+Ext (2012-mi2018) Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00949655.2016.1260726 Date de publication en ligne : 30/11/2016 En ligne : https://doi.org/10.1080/00949655.2016.1260726 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89907
in Journal of Statistical Computation and Simulation > vol 87 n° 6 (2017) . - pp 1255 - 1268[article]Multi-objective based spectral unmixing for hyperspectral images / Xia Xu in ISPRS Journal of photogrammetry and remote sensing, vol 124 (February 2017)PermalinkPulse compression waveform and filter optimization for spaceborne cloud and precipitation radar / Robert M. Beauchamp in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)PermalinkPermalinkModeling spatial and temporal variabilities in hyperspectral image unmixing / Pierre-Antoine Thouvenin (2017)PermalinkPermalinkStatistical inference for forest structural diversity indices using airborne laser scanning data and the k-Nearest Neighbors technique / Matteo Mura in Remote sensing of environment, vol 186 (1 December 2016)PermalinkAn approach for estimating time-variable rates from geodetic time series / Olga Didova in Journal of geodesy, vol 90 n° 11 (November 2016)PermalinkEnabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function / Guiming Zhang in International journal of geographical information science IJGIS, vol 30 n° 11-12 (November - December 2016)PermalinkConvergence of one-step projected gradient methods for variational inequalities / Paul-Emile Maingé in Journal of Optimization Theory and Applications, vol 171 n° 1 (October 2016)PermalinkLinking ecosystem services with state-and-transition models to evaluate rangeland management decisions / Sapana Lohani in Global ecology and conservation, vol 8 (October 2016)Permalink