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Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes / Qingying Yu in International journal of geographical information science IJGIS, vol 35 n° 7 (July 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 7 (July 2021) . - pp 1346 - 1373 Titre : Using information entropy and a multi-layer neural network with trajectory data to identify transportation modes Type de document : Article/Communication Auteurs : Qingying Yu, Auteur ; Yonglong Luo, Auteur ; Dongxia Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1346 - 1373 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par Perceptron multicouche [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] direction [Termes IGN] données spatiotemporelles [Termes IGN] entropie [Termes IGN] extraction de traits caractéristiques [Termes IGN] mobilité urbaine [Termes IGN] Pékin (Chine) [Termes IGN] plan de déplacement urbain [Termes IGN] segmentation [Termes IGN] trajectoire [Termes IGN] vitesse de déplacement Résumé : (auteur) Residents’ trajectory data denote their instantaneous locations along their movements. Mobility research that applies trajectory mining techniques to identify the transportation modes of these movements can inform urban transportation planning. Herein, we propose a five-step approach with information entropy and a multi-layer neural network to identify transportation modes from trajectory data. First, this approach extracts the motion features at each time-stamped location based on foundation geospatial data and spatiotemporal trajectory data, including the speed, acceleration, change of direction, rate of change in direction, and distance from each basic transportation facility. The second step uses information entropy to identify the features that play key roles in identifying transportation modes. The third step weighs each attribute in the feature vector consisting of the selected features and normalizes it to prepare it as input data. The fourth step constructs, trains, and tests a multi-layer neural network with seven-fold cross-validation. The final step includes a post-processing method to optimize the identification result. We use F-measure metric to evaluate the performance. Experimental results on a real trajectory dataset show that the proposed approach can identify the transportation mode at each time-stamped location and outperforms existing transportation-mode identification methods in terms of accuracy and stability. Numéro de notice : A2021-448 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1901904 date de publication en ligne : 15/04/2021 En ligne : https://doi.org/10.1080/13658816.2021.1901904 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97860 [article]

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 : A2021-484 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]

Cloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques / Miao Tian 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 2781 - 2793 Titre : Cloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques Type de document : Article/Communication Auteurs : Miao Tian, Auteur ; Hao Chen, Auteur ; Guanghui Liu, Auteur Année de publication : 2021 Article en page(s) : pp 2781 - 2793 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification par Perceptron multicouche [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection des nuages [Termes IGN] dioxyde de carbone [Termes IGN] image infrarouge [Termes IGN] modèle atmosphérique [Termes IGN] modèle de transfert radiatif [Termes IGN] régression linéaire [Termes IGN] vapeur d'eau Résumé : (auteur) Accurate cloud detection using infrared (IR) data is very challenging due to the limitations and uncertainties from many aspects in the satellite IR remote sensing. This article proposes an end-to-end cloud detection method for the Cross-track IR Sounder (CrIS) using machine learning (ML) techniques. The brightness temperatures from paired CrIS channels in the longwave and midwave water vapor bands and the longwave and shortwave CO 2 bands are used. After obtaining the linear regression coefficients for each of the selected channel pairs, a complete set of CrIS full spectral resolution (FSR) cloud detection index (FCDI) is derived from the temperature difference between the regression and observation for each channel pair. It is shown that FCDI captures cloud location and structure well by comparing with the cloud products (CPs) from the Visible IR Imaging Radiometer Suite (VIIRS). After collocating FCDI with VIIRS CP, ML techniques such as the extreme learning machine, support vector machine, and multilayer perceptron are used to train the collocated FCDIs for cloud detection. Simulation results show that the accuracy of FCDI cloud detection is slightly above 80%. Moreover, the results encourage the use of water vapor bands in FCDI, in addition to CO 2 bands. Numéro de notice : A2021-281 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3020120 date de publication en ligne : 18/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3020120 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97387 [article]

Mapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series / Misganu Debella-Gilo in Remote sensing, Vol 13 n° 2 (January-2 2021)  

Public [article]  inRemote sensing > Vol 13 n° 2 (January-2 2021) . - n° 289 Titre : Mapping seasonal agricultural land use types using deep learning on Sentinel-2 image time series Type de document : Article/Communication Auteurs : Misganu Debella-Gilo, Auteur ; Arnt Kristian Gjertsen, Auteur Année de publication : 2021 Article en page(s) : n° 289 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] carte agricole [Termes IGN] carte d'utilisation du sol [Termes IGN] classification par Perceptron multicouche [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image Sentinel-MSI [Termes IGN] Norvège [Termes IGN] série temporelle [Termes IGN] Soil Adjusted Vegetation Index [Termes IGN] surface cultivée [Termes IGN] utilisation du sol [Termes IGN] variation saisonnière Résumé : (auteur) The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas. Numéro de notice : A2021-198 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13020289 date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.3390/rs13020289 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97149 [article]

Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India / Sunil Saha in Geomatics, Natural Hazards and Risk, vol 12 n° 1 (2021)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 12 n° 1 (2021) . - pp 29 - 62 Titre : Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India Type de document : Article/Communication Auteurs : Sunil Saha, Auteur ; Gopal Chandra, Auteur ; Biswajeet Pradhan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 29 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] bagging [Termes IGN] changement d'occupation du sol [Termes IGN] classification hybride [Termes IGN] classification par Perceptron multicouche [Termes IGN] déboisement [Termes IGN] Inde [Termes IGN] modèle de simulation [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] Rotation Forest classification [Termes IGN] système d'information géographique Résumé : (auteur) The rapid expansion of human settlement, agricultural land and roads because of population growth in several regions of the world has contributed to the depletion of forest land. In this study, novel ensemble intelligent approaches using bagging, dagging and rotation forest (RTF) as meta classifiers of multilayer perceptron (MLP) were used to predict spatial deforestation probability (DP) in Gumani Basin, India. The success rate and correctness of prediction of the ensemble models were compared with MLP. A total of 1000 deforested pixels and 14 deforestation determining factors (DDFs) were used. The ensemble models were trained using 70% of the deforested pixels and validated with the remaining 30%. DDFs were chosen by applying the information gain ratio and Relief-F test methods. Distance to settlement, population growth and distance to roads were the most important factors. The results of DP modelling demonstrated that nearly 16.82%–12.64% of the basin had very high DP. All four models created DP maps with reasonable prediction accuracy and goodness of fit, but the best map was produced by MLP-bagging. The accuracy of the MLP neural net model was increased 2-3% after ensemble with the hybrid meta classifiers (RTF, bagging and dagging). The proposed method could be used for deforestation prediction in other areas having similar geo-environmental conditions. Furthermore, the findings might be used as a basis for future research and could help planners in forest management. Numéro de notice : A2021-106 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475705.2020.1860139 date de publication en ligne : 22/12/2020 En ligne : https://doi.org/10.1080/19475705.2020.1860139 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96903 [article]

Learning harmonised Pleiades and Sentinel-2 surface reflectances / J. Michel (2021)  

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Application of digital image processing in automated analysis of insect leaf mines / Yee Man Theodora Cho (2020)  

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Convolutional neural networks for change analysis in earth observation images with noisy labels and domain shifts / Rodrigo Caye Daudt (2020)  

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