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Object-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery / Zhenhui Sun in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

Public [article]  inGeocarto international > Vol 35 n° 8 [01/06/2020] . - pp 801 - 817 Titre : Object-based automatic multi-index built-up areas extraction method for WorldView-2 satellite imagery Type de document : Article/Communication Auteurs : Zhenhui Sun, Auteur ; Qingyan Meng, Auteur Année de publication : 2020 Article en page(s) : pp 801 - 817 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] Normalized Difference Water Index [Termes descripteurs IGN] optimisation par essaim de particules [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) The WorldView-2 high spatial resolution satellite with eight multispectral imaging bands is ideally suited for extracting built-up areas (BUs) from remote sensing images. In this study, an object-based automatic multi-index BUs extraction method was developed. First, several indices, including BUs extraction index (NBEIr-c), vegetation extraction index(NDVInir2-r) and water extraction index (NDWI b-nir1), were developed to obtain the BUs, vegetation and water maps, and then the fractional-order Darwinian particle swarm optimization (FODPSO) algorithm was employed to automatically segment the multi-index images and obtained BUs, water, vegetation and bare soil (BS) information. Finally, the extracted BUs results were optimized via an object-based analysis method and the results were compared with those of two other relevant indices, which confirmed the proposed method had a higher accuracy and exhibited higher performance when separating the BS from the BUs. Numéro de notice : A2020-273 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1544290 date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1544290 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95058 [article]

Generation of digital terrain model for forest areas using a new particle swarm optimization on LiDAR data / Behnaz Bigdeli in Survey review, vol 52 n° 371 (March 2020)  

Public [article]  inSurvey review > vol 52 n° 371 (March 2020) . - pp 115 - 125 Titre : Generation of digital terrain model for forest areas using a new particle swarm optimization on LiDAR data Type de document : Article/Communication Auteurs : Behnaz Bigdeli, Auteur ; Masoomeh Gomroki, Auteur ; Parham Pahlavani, Auteur Année de publication : 2020 Article en page(s) : pp 115 - 125 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] erreur moyenne quadratique [Termes descripteurs IGN] filtrage de la végétation [Termes descripteurs IGN] interpolation polynomiale [Termes descripteurs IGN] Iran [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] optimisation par essaim de particules [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] surface forestière Résumé : (auteur) Since Light Detection and Ranging (LiDAR) data are capable of distinguishing vegetation from bare earth, these data are used nowadays to produce digital terrain models (DTMs) for forest regions. In this research, raw LiDAR data were filtered using hybrid and slope-based filtering methods and the filtered data were then interpolated using the new modified particle swarm optimisation (PSO) and accordingly the results were compared with those achieved by the other intelligent and conventional interpolation methods. The new modified PSO optimized the polynomial degree for interpolation and found suitable parameters for optimisation. Two data sets from two forest regions in some northern regions of Iran located in Golestan province were selected to compare these methods. Region 1 with dense vegetation and region 2 with grass vegetation. The results indicated that the hybrid filter performed lower RMSE than the slope-based filter. Finally, the DTM with lowest RMSE was obtained using the hybrid filter and the modified PSO interpolation method with RMSE of 6 mm for region 1 (Tavar-kuh) and 61 mm for region 2 (Shastkola River Basin). Numéro de notice : A2020-078 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1530331 date de publication en ligne : 10/10/2018 En ligne : https://doi.org/10.1080/00396265.2018.1530331 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94640 [article]

Simulating urban growth processes by integrating cellular automata model and artificial optimization in Binhai New Area of Tianjin, China / Fengmei Yao in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)  

Public [article]  inGeocarto international > vol 31 n° 5 - 6 (May - June 2016) . - pp 612 - 627 Titre : Simulating urban growth processes by integrating cellular automata model and artificial optimization in Binhai New Area of Tianjin, China Type de document : Article/Communication Auteurs : Fengmei Yao, Auteur ; Cui Hao, Auteur ; Jiahua Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 612 - 627 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] Chine [Termes descripteurs IGN] intelligence artificielle [Termes descripteurs IGN] optimisation par essaim de particules [Termes descripteurs IGN] simulation [Termes descripteurs IGN] surveillance de l'urbanisation [Termes descripteurs IGN] urbanisation Résumé : (Auteur) This study presents an optimized algorithm into the cellular automata (CA) models for urban growth simulation in Binhai New Area of Tianjin, China. The optimized CA model by particle swarm optimization (PSO) was compared with the logistic-based cellular automata (LOGIT-CA) model to see the effects of the simulation. The study evaluated the stochastic disturbance in the development of urban growth using the Monte Carlo method; the coefficient d determined the state of urban growth. The validation was conducted by both cross-tabulation test and structural measurements. The results showed that the simulations of PSO-CA were better than LOGIT-CA model, indicating an improvement in the spatio-temporal simulation of urban growth and land use changes in study area. Since the simulations reached their best values when the coefficient was between 1 and 2, the urban growth in the study area was in the period of conversion from spontaneous growth to edge-expansion and infilling growth. Numéro de notice : A2016-172 Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1073365 date de publication en ligne : 14/08/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1073365 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80516 [article]

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Road vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation / Fateme Ameri in Photogrammetric record, vol 30 n° 152 (December 2015 - February 2016)  

Public [article]  inPhotogrammetric record > vol 30 n° 152 (December 2015 - February 2016) . - pp 363 - 386 Titre : Road vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation Type de document : Article/Communication Auteurs : Fateme Ameri, Auteur ; Mohammad Javad Valadan Zoej, Auteur Année de publication : 2015 Article en page(s) : pp 363 - 386 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification automatique d'objets [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image Ikonos [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] optimisation par essaim de particules [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] vectorisation Résumé : (auteur) This paper introduces an innovative automatic road-vectorisation algorithm based on dynamic pixel clustering using particle swarm optimisation. A new cost function is designed to optimise the number and position of road keypoints and is capable of deriving road centrelines without considering geometric, spectral or topological characteristics in the road model. The algorithm is applied to different high-resolution images (IKONOS, QuickBird and aerial photographs) and is evaluated with respect to RMSE, correctness and completeness. Moreover, a new quality parameter is defined to evaluate a “kinking” effect in roads. Extraction of different road shapes with an acceptable precision in both urban and rural environments proves the efficiency of the algorithm in yielding complete road networks. Numéro de notice : A2015-827 Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12123 date de publication en ligne : 15/12/2015 En ligne : https://doi.org/10.1111/phor.12123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79123 [article]

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