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Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([15/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 8 [15/04/2021] . - pp 918 - 935 Titre : Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; İlker Ercanlı, Auteur ; Muammer Şenyurt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 918 - 935 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] classification par Perceptron multicouche [Termes IGN] échantillonnage [Termes IGN] fonction de base radiale [Termes IGN] gestion forestière [Termes IGN] image proche infrarouge [Termes IGN] image Worldview [Termes IGN] matrice de co-occurrence [Termes IGN] peuplement forestier [Termes IGN] Pinus nigra [Termes IGN] régression multiple [Termes IGN] réseau neuronal artificiel [Termes IGN] texture d'image [Termes IGN] Turquie Résumé : (auteur) The aim of this research is to assess some stand parameters such as stand volume (SV), basal area (BA), number of trees (NT) and aboveground biomass (AGB) of pure Crimean pine forest stands in Turkey by using ground measurements and remote sensing techniques. For this purpose, 86 sample plots were collected from pure Crimean pine stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. The stand parameters of each sample area were estimated using the data obtained from the sample plots. Subsequently, we calculated the values of contrast (CON), correlation (COR), dissimilarity (DIS), entropy (ENT), homogeneity (HOM), mean (M), second moment (SM) and variance (VAR) from WorldView-2 imagery using a grey-level co-occurrence matrix method. Eight textural features and twelve different window sizes ranging from 3 × 3 to 25 × 25 were generated from blue, green, red and near-infrared bands of the WorldView-2 satellite image. For predicting the relationships between WorldView-2 textural features and stand parameters of each sample plot, regression models were developed by using multiple linear regression (MLR) analysis. Additionally, artificial neural networks (ANNs) based on the multilayer perceptron (MLP) and the radial basis function (RBF) architectures were trained by comparing various numbers of neurons and activation functions in their network types. The results showed that the MLR models had low the coefficient of determination (R2) values (0.32 for SV, 0.35 for BA, 0.33 for NT and 0.34 for AGB), and the most of the ANNs models (MLP and RBF) were better than the regression models for estimating stand parameters. The ANNs model containing MLP and RBF for SV (R2 = 0.40; R2 = 0.56), for BA (R2 = 0.34; R2 = 0.51), for NT (R2 = 0.34; R2 = 0.37) and for AGB (R2 = 0.34, R2 = 0.57) were found the best results, respectively. Our results revealed that the ANNs models developed with WorldView-2 satellite image were beneficial to estimate stand parameters better than the MLR model in pure Crimean pine stands. Numéro de notice : A2021-308 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1629644 date de publication en ligne : 25/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1629644 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97443 [article]

Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3040 - 3053 Titre : Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data Type de document : Article/Communication Auteurs : Xiao-Ming Li, Auteur ; Yan Sun, Auteur ; Qiang Zhang, Auteur Année de publication : 2021 Article en page(s) : pp 3040 - 3053 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Arctique, océan [Termes IGN] classification non dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données d'apprentissage [Termes IGN] entropie [Termes IGN] extraction de traits caractéristiques [Termes IGN] glace de mer [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] matrice de co-occurrence [Termes IGN] niveau de gris (image) [Termes IGN] polarisation croisée [Termes IGN] rétrodiffusion [Termes IGN] texture Résumé : (auteur) In this article, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization [vertical–horizontal (VH) or horizontal–vertical (HV)] data in extra-wide (EW) swath mode based on the support vector machine (SVM) method. The classification basis includes the S1 radar backscatter and texture features, which are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e., entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparisons based on a few cases show good agreements between the synthetic aperture radar (SAR)-derived sea ice cover using the proposed method and visual inspections, of which the accuracy reaches approximately 90%–95%. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of the extracted sea ice cover by using S1 data is more than 80%. Numéro de notice : A2021-284 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3007789 date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3007789 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97392 [article]

Land cover harmonization using Latent Dirichlet Allocation / Zhan Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 348 - 374 Titre : Land cover harmonization using Latent Dirichlet Allocation Type de document : Article/Communication Auteurs : Zhan Li, Auteur ; Joanne C. White, Auteur ; Michael A. Wulder, Auteur Année de publication : 2021 Article en page(s) : pp 348 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] allocation de Dirichlet latente [Termes IGN] Canada [Termes IGN] carte d'occupation du sol [Termes IGN] chevauchement [Termes IGN] erreur de classification [Termes IGN] étiquetage sémantique [Termes IGN] harmonisation des données [Termes IGN] matrice d'erreur [Termes IGN] matrice de co-occurrence [Termes IGN] utilisation du sol Résumé : (auteur) Large-area land cover maps are produced to satisfy different information needs. Land cover maps having partial or complete spatial and/or temporal overlap, different legends, and varying accuracies for similar classes, are increasingly common. To address these concerns and combine two 30-m resolution land cover products, we implemented a harmonization procedure using a Latent Dirichlet Allocation (LDA) model. The LDA model used regionalized class co-occurrences from multiple maps to generate a harmonized class label for each pixel by statistically characterizing land attributes from the class co-occurrences. We evaluated multiple harmonization approaches: using the LDA model alone and in combination with more commonly used information sources for harmonization (i.e. error matrices and semantic affinity scores). The results were compared with the benchmark maps generated using simple legend crosswalks and showed that using LDA outputs with error matrices performed better and increased harmonized map overall accuracy by 6–19% for areas of disagreement between the source maps. Our results revealed the importance of error matrices to harmonization, since excluding error matrices reduced overall accuracy by 4–20%. The LDA-based harmonization approach demonstrated in this paper is quantitative, transparent, portable, and efficient at leveraging the strengths of multiple land cover maps over large areas. Numéro de notice : A2021-027 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1796131 date de publication en ligne : 27/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1796131 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96701 [article]

Unmixing-based Sentinel-2 downscaling for urban land cover mapping / Fei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 133 - 154 Titre : Unmixing-based Sentinel-2 downscaling for urban land cover mapping Type de document : Article/Communication Auteurs : Fei Xu, Auteur ; Ben Somers, Auteur Année de publication : 2021 Article en page(s) : pp 133 - 154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] bande spectrale [Termes IGN] Berlin [Termes IGN] Bruxelles [Termes IGN] cartographie urbaine [Termes IGN] Cologne [Termes IGN] corrélation [Termes IGN] fusion d'images [Termes IGN] image Sentinel-MSI [Termes IGN] matrice de co-occurrence [Termes IGN] occupation du sol Résumé : (auteur) With the launch of Sentinel-2 new opportunities for large scale urban mapping arise. However, the spectral information embedded in the Sentinel-2 20 m spatial resolution bands cannot yet be fully explored in heterogeneous urban landscapes. The 20 m image pixels are often composed of different land covers, resulting in a difficult to interpret mixed pixel spectrum. Here, we propose an unmixing-based image fusion algorithm (UnFuSen2) that self-adapts to the spectral variability of varying land covers and improves the image fusion accuracy by constraining the unmixing equations on the basis of spectral mixing models and the correlation between spectral bands of coarse and fine spatial resolution, respectively. When compared to alternative state-of-the-art downscaling methods UnFuSen2 consistently showed the highest accuracy when applied across test sites in three different European cities (RMSEUnFuSen2 = 203 vs RMSEalternatives = [252, 337]). In a next step, we applied Multiple Endmember Spectral Mixture Analysis (MESMA) on the downscaled Sentinel-2 image cube (i.e. ten 10 m bands) to generate subpixel urban land cover fractions. We compared our MESMA results against the traditional MESMA output as applied on the original Sentinel-2 image cube (i.e. four 10 m bands and six 20 m bands) and tested its robustness against reference data obtained over all three study sites. Results revealed an average decrease in RMSE of respectively 18% and 8% for impervious surface and vegetation fractions when our approach was compared to the traditional MESMA outcomes. Numéro de notice : A2021-015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.009 date de publication en ligne : 26/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96419 [article]

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How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data / Chaogui Kang in Transactions in GIS, Vol 24 n° 6 (December 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 6 (December 2020) . - pp 1504 - 1525 Titre : How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data Type de document : Article/Communication Auteurs : Chaogui Kang, Auteur ; Li Shi, Auteur ; Fahui Wang, Auteur ; Yu Liu, Auteur Année de publication : 2020 Article en page(s) : pp 1504 - 1525 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Chine [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données spatiotemporelles [Termes IGN] ethnographie [Termes IGN] factorisation de matrice non-négative [Termes IGN] matrice de co-occurrence [Termes IGN] production participative [Termes IGN] réseau social [Termes IGN] site urbain [Termes IGN] téléphonie mobile [Termes IGN] urbanisme Résumé : (Auteur) This research attempts to build a unified framework for distinguishing the spatiotemporal visit patterns of urban places by different social groups using mobile phone data in Harbin, China. Social groups are detected by their social ties in the ego‐to‐ego mobile phone call network and are embedded in physical space according to their home locations. Popular urban places are detected from user‐generated content as the basic spatial analysis unit. Coupling subscribers’ footprints and urban places in physical space, the spatiotemporal visit patterns of urban places by distinct social groups are uncovered and interpreted by non‐negative matrix factorization. The proposed framework enables us to answer several critical questions from three perspectives: (1) How to model popular urban places in terms of vague boundary, land use, and semantic features based on crowdsourcing data?; (2) How to evaluate interaction between individuals for inspecting the relationship between spatial proximity and social ties based on spatiotemporal co‐occurrence?; and (3) How to distinguish urban place visit preferences for social groups associated with different socio‐demographic characteristics? Our research could assist urban planners and municipal managers to identify critical urban places frequented by different population groups according to their roles and social/cultural characteristics for improvement of urban facility allocation. Numéro de notice : A2020-767 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12654 date de publication en ligne : 30/06/2020 En ligne : https://doi.org/10.1111/tgis.12654 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96658 [article]

Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])  

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Extracting impervious surfaces from full polarimetric SAR images in different urban areas / Sara Attarchi in International Journal of Remote Sensing IJRS, vol 41 n°12 (20 - 30 March 2020)  

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Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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Estimating forest canopy cover in black locust (Robinia pseudoacacia L.) plantations on the loess plateau using random forest / Qingxia Zhao in Forests, vol 9 n° 10 (October 2018)  

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Remote sensing of species diversity using Landsat 8 spectral variables / Sabelo Madonsela in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

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Discovering non-compliant window co-occurrence patterns / Reem Y. Ali in Geoinformatica [en ligne], vol 21 n° 4 (October - December 2017)  

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The analysis and measurement of building patterns using texton co-occurrence matrices / Wenhao Yu in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

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Deep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

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Analyzing relatedness by toponym co-occurrences on web pages / Yu Liu in Transactions in GIS, vol 18 n° 1 (February 2014)  

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Information content of very high resolution SAR images: study of feature extraction and imaging parameters / Corneliu Dimitru in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)  

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