Centre dedocumentation
scientifique

Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones / Xun Liang in International journal of geographical information science IJGIS, vol 34 n° 10 (October 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 10 (October 2020) . - pp 1930 - 1952 Titre : Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones Type de document : Article/Communication Auteurs : Xun Liang, Auteur ; Xiaoping Liu, Auteur ; Guangliang Chen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1930 - 1952 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] aide à la décision [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] planification urbaine [Termes descripteurs IGN] zone d'activité économique Résumé : (auteur) Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (CA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified CA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, China from 2013 to 2019. The proposed model is applied to future simulation in 2019–2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs. Numéro de notice : A2020-513 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1741591 date de publication en ligne : 23/03/2020 En ligne : https://doi.org/10.1080/13658816.2020.1741591 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95668 [article]

A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments / Elahe S. Abdolkarimi in GPS solutions, Vol 24 n° 4 (October 2020)  

Public [article]  inGPS solutions > Vol 24 n° 4 (October 2020) . - 19 p. Titre : A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments Type de document : Article/Communication Auteurs : Elahe S. Abdolkarimi, Auteur ; Mohammad-Reza Mosavi, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes descripteurs IGN] centrale inertielle [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] GPS-INS [Termes descripteurs IGN] microsystème électromécanique [Termes descripteurs IGN] modèle d'incertitude [Termes descripteurs IGN] rapport signal sur bruit [Termes descripteurs IGN] transformation en ondelettes Résumé : (auteur) Integration of both global positioning system (GPS) and inertial navigation system (INS) assures a continuous and accurate navigation system. In low-cost low-precision micro-electromechanical system (MEMS)-based INS/GPS integration navigation systems, one of the major concerns is high-level stochastic noise and uncertainties existing in INS sensors and complex model of real noisy data. In such uncertainty-oriented environments, an intelligence structure with extra degrees of freedom which can handle and model a high-level of uncertainties in INS sensors, and an efficient denoising technique as a precursor to the intelligence structure can be efficient solutions. Our approach to these problems is taken in different steps. First, a denoising technique based on empirical mode decomposition (EMD) is used to provide more accurate INS sensor outputs and better generalization ability. Second, an optimized interval type-2 fuzzy neural network is used to model and handle a high-level of uncertainties efficiently and estimate the positioning error of INS sensors when GPS signals are blocked, and still meet both accuracy maximization and complexity minimization. Fast learning and convergence of the algorithm and less computational complexity can be achieved by using an extended Kalman filter in the learning of algorithm and an accurate and simple type-reduction, respectively, which can be utilized in real-time applications with significant performance. The results of EMD-based denoising technique, as a preprocessing phase, verify superior performance in comparison with the discrete wavelet transform denoising method in the signal-to-noise ratio improvement for raw and noisy signals of INS sensors. To verify the effectiveness of our proposed model, we applied challenging conditions consisting of low-cost low-precision inertial sensors based on MEMS technology, long-term outages of GPS satellites, a high-speed experimental test vehicle and noisy real-world data in the real-time flight experiments. The achieved experimental accuracies are compared with the results that we have achieved in other methods, and our proposed method verifies significant improvements. Numéro de notice : A2020-521 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01023-9 date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1007/s10291-020-01023-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95692 [article]

Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods / Rocio Nahime Torres in Applied geomatics, vol 12 n° 2 (June 2020)  

Public [article]  inApplied geomatics > vol 12 n° 2 (June 2020) . - pp 225 – 246 Titre : Mountain summit detection with Deep Learning: evaluation and comparison with heuristic methods Type de document : Article/Communication Auteurs : Rocio Nahime Torres, Auteur Année de publication : 2020 Article en page(s) : pp 225 – 246 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données altimétriques [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] collecte de données [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] figuré du terrain [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] montagne [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] sommet (relief) [Termes descripteurs IGN] système d'information géographique Résumé : (auteur) Landform detection and analysis from Digital Elevation Models (DEM) of the Earth has been boosted by the availability of high-quality public data sets. Current landform identification methods apply heuristic algorithms based on predefined landform features, fine tuned with parameters that may depend on the region of interest. In this paper, we investigate the use of Deep Learning (DL) models to identify mountain summits based on features learned from data examples. We train DL models with the coordinates of known summits found in public databases and apply the trained models to DEM data obtaining as output the coordinates of candidate summits. We introduce two formulations of summit recognition (as a classification or a segmentation task), describe the respective DL models, compare them with heuristic methods quantitatively, illustrate qualitatively their performances, and discuss the challenges of training DL methods for landform recognition with highly unbalanced and noisy data sets. Numéro de notice : A2020-560 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00295-2 date de publication en ligne : 24/12/2019 En ligne : https://doi.org/10.1007/s12518-019-00295-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95870 [article]

Hyperspectral image clustering with Albedo recovery Fuzzy C-Means / P. Azimpour in International Journal of Remote Sensing IJRS, vol 41 n°16 (01-10 May 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n°16 (01-10 May 2020) . - pp 6117 - 6134 Titre : Hyperspectral image clustering with Albedo recovery Fuzzy C-Means Type de document : Article/Communication Auteurs : P. Azimpour, Auteur ; R. Shad, Auteur ; M. Ghaemi, Auteur ; H. Etemadfard, Auteur Année de publication : 2020 Article en page(s) : pp 6117 - 6134 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] albedo [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] télédétection Résumé : (auteur) Hyperspectral image clustering is usually used for unsupervised learning in different applications. However, the traditional clustering methods have not been considered the complex relationships among neighbouring pixels. The Albedo and Shading elements can define pixel values in the HyperSpectral Images (HSIs). In HSIs, features are different from each other because of their natural physical characteristics and the physical nature of different image features can be described by the Albedo element. Therefore, in this paper, we generate the natural Albedo feature of the HSIs by applying Albedo recovery step to exploit main information from HSIs. Then, we utilized the Fuzzy C-means clustering method to cluster the natural Albedo dataset. In this paper, we propose a novel accurate Albedo Recovery based Fuzzy C-Means (ARFCM) method to cluster HSIs. In the dataset, each feature vector is processed by the Albedo recovery step to create a new feature vector. This new feature vector can describe the dataset better than the original one. Comparing clustering methods as one of the powerful clustering algorithms are widely used in the remote sensing fields of studying. The experiments conducted on several benchmark datasets demonstrated that the proposed clustering method achieves higher performance than other methods and present the efficiency and effectiveness of the proposed method. The results of experiments over different HSI datasets indicated that the proposed method could produce reliable and suitable results compared to the other methods. This shows the robustness of the proposed ARFCM algorithm over the various HSI datasets. Other methods may provide a good response in a given dataset and do not perform well in the other data. Consequently, the ARFCM method, regardless of the study area characteristics and the sensor features, always renders remarkable clustering accuracy. Numéro de notice : A2020-453 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1736728 date de publication en ligne : 01/06/2020 En ligne : https://doi.org/10.1080/01431161.2020.1736728 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95542 [article]

Multi-factor of path planning based on an ant colony optimization algorithm / Mingchang Wang in Annals of GIS, vol 26 n° 2 (April 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 2 (April 2020) Titre : Multi-factor of path planning based on an ant colony optimization algorithm Type de document : Article/Communication Auteurs : Mingchang Wang, Auteur ; Chunyu Zhu, Auteur ; Fengyan Wang, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] gestion des itinéraires [Termes descripteurs IGN] optimisation par colonie de fourmis [Termes descripteurs IGN] planification [Termes descripteurs IGN] processus d'analyse hiérarchisée floue [Termes descripteurs IGN] robot Résumé : (auteur) We propose an improved ant colony algorithm for avoiding obstacles in complex static environments that addresses the problems of a single evaluation factor and low path quality of the traditional ant colony algorithm in path planning. The improvements are: 1) a fuzzy planner is constructed according to the comprehensive evaluation method of fuzzy mathematics and the analytic hierarchy process to comprehensively evaluate and determine the impact of environmental factors, 2) the probability selection formula of the ant colony algorithm is optimized, 3) the pheromone update formula is optimized, and 4) the corner system mechanism is introduced as a post-processing method of path optimization to further smooth the path. Results from simulation experiments of the traditional ant colony algorithm were analysed and compared with those of the improved ant colony algorithm, showing that the latter has a stronger path planning ability and higher algorithm efficiency, resulting in a smoother path with a lower negative impact by environmental factors. Thus, the proposed algorithm is expected to provide a computational basis for effective multi-factor path planning in realistic environments, thereby saving human and material resources. Numéro de notice : A2020-320 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2020.1755725 date de publication en ligne : 13/05/2020 En ligne : https://doi.org/10.1080/19475683.2020.1755725 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95186 [article]

Multi-spectral image change detection based on single-band iterative weighting and fuzzy C-means clustering / Liyuan Ma in European journal of remote sensing, vol 53 n°1 (2020)  

Permalink

INS/GNSS integration using recurrent fuzzy wavelet neural networks / Parisa Doostdar in GPS solutions, vol 24 n° 1 (January 2020)  

Permalink

Saliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

Permalink

Tree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 (2019)  

Permalink

Airborne Lidar/INS/GNSS : algorithm uses fuzzy controlled Scale Invariant Feature Transform (SIFT) / Haowei Xu in GPS world, vol 28 n° 3 (March 2017)

Permalink

The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering / Yi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)  

Permalink

Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information / Alexis Comber in Geoinformatica [en ligne], vol 20 n° 3 (July - September 2016)  

Permalink

An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery / Shuli Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

Permalink

Application of fuzzy combination operators to flood vulnerability assessments in Seoul, Korea / Moung-Jin Lee in Geocarto international, vol 30 n° 9 - 10 (October - November 2015)  

Permalink

Classification of remotely sensed images using the geneSIS fuzzy segmentation algorithm / Stelios Mylonas in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)  

Permalink