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Aboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models / Dibyendu Deb in Geocarto international, vol 37 n° 4 (April 2022)  

Public [article]  inGeocarto international > vol 37 n° 4 (April 2022) . - pp 1043 - 1058 Titre : Aboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models Type de document : Article/Communication Auteurs : Dibyendu Deb, Auteur ; Shovik Deb, Auteur ; Debasis Chakraborty, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1043 - 1058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biomasse aérienne [Termes IGN] distribution spatiale [Termes IGN] image Landsat-8 [Termes IGN] Inde [Termes IGN] indice de végétation [Termes IGN] modèle de régression [Termes IGN] point d'appui [Termes IGN] régression linéaire [Termes IGN] régression multiple [Termes IGN] séparateur à vaste marge [Termes IGN] zone semi-aride Résumé : (auteur) This study compared the traditional regression models and support vector machine (SVM) for estimation of aboveground biomass (ABG) of an agro-pastoral ecology using vegetation indices derived from Landsat 8 satellite data as explanatory variables . The area falls in the Shivpuri Tehsil of Madhya Pradesh, India, which is predominantly a semi-arid tract of the Bundelkhand region. The Enhanced Vegetation Index-1 (EVI-1) was identified as the most suitable input variable for the regression models, although the collective effect of a number of the vegetation indices was evident. The EVI-1 was also the most suitable input variable to SVM, due to its capacity to distinctly differentiate diverse vegetation classes. The performance of SVM was better over regression models for estimation of the AGB. Based on the SVM-derived and the ground observations, the AGB of the area was precisely mapped for croplands, grassland and rangelands over the entire region. Numéro de notice : A2022-394 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1756461 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1756461 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100688 [article]

Extraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning / Jun Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 3 (March 2022) . - pp 199 - 205 Titre : Extraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning Type de document : Article/Communication Auteurs : Jun Xu, Auteur ; Jiasong Li, Auteur ; Hao Peng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 199 - 205 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse d'image orientée objet [Termes IGN] classification barycentrique [Termes IGN] distance de Kullback-Leibler [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] image Worldview [Termes IGN] masque [Termes IGN] occupation du sol [Termes IGN] segmentation d'image [Termes IGN] segmentation multi-échelle [Termes IGN] séparateur à vaste marge Résumé : (auteur) In object-oriented information extraction from high-resolution remote sensing images, the segmentation and classification of images involves considerable manual participation, which limits the development of automation and intelligence for these purposes. Based on the multi-scale segmentation strategy and case-based reasoning, a new method for extracting high-resolution remote sensing image information by fully using the image and nonimage features of the case object is proposed. Feature selection and weight learning are used to construct a multi-level and multi-layer case library model of surface cover classification reasoning. Combined with image mask technology, this method is applied to extract surface cover classification information from remote sensing images using different sensors, time, and regions. Finally, through evaluation of the extraction and recognition rates, the accuracy and effectiveness of this method was verified. Numéro de notice : A2022-202 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00104R3 Date de publication en ligne : 01/03/2022 En ligne : https://doi.org/10.14358/PERS.20-00104R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100006 [article]

A robust nonrigid point set registration framework based on global and intrinsic topological constraints / Guiqiang Yang in The Visual Computer, vol 38 n° 2 (February 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 2 (February 2022) . - pp 603 - 623 Titre : A robust nonrigid point set registration framework based on global and intrinsic topological constraints Type de document : Article/Communication Auteurs : Guiqiang Yang, Auteur ; Rui Li, Auteur ; Yujun Liu, Auteur ; Ji Wang, Auteur Année de publication : 2022 Article en page(s) : pp 603 - 623 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme espérance-maximisation [Termes IGN] contrainte géométrique [Termes IGN] contrainte topologique [Termes IGN] descripteur local [Termes IGN] méthode fondée sur le noyau [Termes IGN] méthode robuste [Termes IGN] processus gaussien [Termes IGN] semis de points [Termes IGN] superposition de données Résumé : (auteur) The problem of registering nonrigid point sets, with the aim of estimating the correspondences and learning the transformation between two given sets of points, often arises in computer vision tasks. This paper proposes a novel method for performing nonrigid point set registration on data with various types of degradation, in which the registration problem is formulated as a Gaussian mixture model (GMM)-based density estimation problem. Specifically, two complementary constraints are jointly considered for optimization in a GMM probabilistic framework. The first is a thin-plate spline-based regularization constraint that maintains global spatial motion consistency, and the second is a spectral graph-based regularization constraint that preserves the intrinsic structure of a point set. Moreover, the correspondences and the transformation are alternately optimized using the expectation maximization algorithm to obtain a closed-form solution. We first utilize local descriptors to construct the initial correspondences and then estimate the underlying transformation under the GMM-based framework. Experimental results on contour images and real images show the effectiveness and robustness of the proposed method. Numéro de notice : A2022-146 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-020-02037-7 Date de publication en ligne : 21/02/2022 En ligne : https://doi.org/10.1007/s00371-020-02037-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100040 [article]

Siamese Adversarial Network for image classification of heavy mineral grains / Huizhen Hao in Computers & geosciences, vol 159 (February 2022)  

Public [article]  inComputers & geosciences > vol 159 (February 2022) . - n° 105016 Titre : Siamese Adversarial Network for image classification of heavy mineral grains Type de document : Article/Communication Auteurs : Huizhen Hao, Auteur ; Zhiwei Jiang, Auteur ; Shiping Ge, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage profond [Termes IGN] classification barycentrique [Termes IGN] classification et arbre de régression [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] extraction de traits caractéristiques [Termes IGN] microscope électronique [Termes IGN] minéral [Termes IGN] polarisation croisée [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau neuronal siamois [Termes IGN] séparateur à vaste marge Résumé : (auteur) The identification of heavy mineral grains based on microscopic images can significantly reduce the time and economic cost of the identification. There are several deep learning models to realize end-to-end identification of mineral image recently. However, due to the variety and complexity of mineral images, the existing models are difficult to accurately recognize heavy mineral grains in microscopic images. Here we propose the Siamese Adversarial Network (SAN) for image classification of the heavy mineral grains, which is the first time to focus on addressing the domain difference of heavy mineral images from different basins. In more details, we design a Siamese feature encoder to extract features of both the plane-polarized and cross-polarized images as internal representation of heavy mineral grains. The features are reconstructed to discard domain-related information by adversarial training the heavy mineral classifier and domain discriminator. The identification performance of the models under the three mixed domain experiments is consistently higher than the performance under the same domain settings respectively which shows that the model we proposed achieves a great generalization ability on unseen domains. Numéro de notice : A2022-174 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.105016 Date de publication en ligne : 03/12/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.105016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99810 [article]

Flood susceptibility mapping using meta-heuristic algorithms / Alireza Arabameri in Geomatics, Natural Hazards and Risk, vol 13 n° 1 (2022)  

Public [article]  inGeomatics, Natural Hazards and Risk > vol 13 n° 1 (2022) . - pp 949 - 974 Titre : Flood susceptibility mapping using meta-heuristic algorithms Type de document : Article/Communication Auteurs : Alireza Arabameri, Auteur ; Amir Seyed Danesh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 949 - 974 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme génétique [Termes IGN] base de données localisées [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] Google Earth [Termes IGN] inondation [Termes IGN] Iran [Termes IGN] optimisation par essaim de particules [Termes IGN] SAGA GIS [Termes IGN] séparateur à vaste marge [Termes IGN] traitement de données localisées [Termes IGN] vulnérabilité [Termes IGN] zone à risque Résumé : (auteur) Flood is a common global natural hazard, and detailed flood susceptibility maps for specific watersheds are important for flood management measures. We compute the flood susceptibility map for the Kaiser watershed in Iran using machine learning models such as support vector machine (SVM), Particle swarm optimization (PSO), and genetic algorithm (GA) along with ensembles (PSO-GA and SVM-GA). The application of such machine learning models in flood susceptibility assessment and mapping is analyzed, and future research suggestions are presented. The model of flood susceptibility model was constructed based on fifteen causatives: slope, slope aspect, elevation, plan curvature, land use, and land cover, normalize differences vegetation index (NDVI), convergence index (CI), topographical wetness index (TWI), topographic positioning Index (TPI), drainage density (DD), distance to stream, terrain ruggedness index (TRI), terrain surface texture (TST), geology and stream power index (SPI) and flood inventory data which later is divided by 70% for training the model and 30% for validated the model. The model output was evaluated through sensitivity, specificity, accuracy, precision, Cohen Kappa, F-score, and receiver operating curve (ROC). The evaluation of flood susceptibility mapping through the receiver operating curve method along with flood density shows robust results from support vector machine (0.839), particle swarm optimization (0.851), genetic algorithm (0.874), SVM-GA (0.886), and PSO-GA (0.902). Compared have done with some methods commonly used in this susceptibility assessment. A high-quality, informative database is essential for the classification of flood types in flood susceptibility mapping that is very important and helpful to improve the model performances. The performance of the ensemble PSO-GA is better than that of the machine learning model, yielding a high degree of accuracy (AUC-0.902%). Our approach, therefore, provides a novel method for flood susceptibility studies in other watersheds. Numéro de notice : A2022-300 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/19475705.2022.2060138 Date de publication en ligne : 11/04/2022 En ligne : https://doi.org/10.1080/19475705.2022.2060138 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100383 [article]

Introduction au Machine Learning / Chloé-Agathe Azencott (2022) 

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A comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping / Khalil Valizadeh Kamran in Applied geomatics, vol 13 n° 4 (December 2021)  

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Spatially–encouraged spectral clustering: a technique for blending map typologies and regionalization / Levi John Wolf in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

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Multiscale context-aware ensemble deep KELM for efficient hyperspectral image classification / Bobo Xi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

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Retrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean / Kunpeng Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

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Machine learning and geodesy: A survey / Jemil Butt in Journal of applied geodesy, vol 15 n° 2 (April 2021)  

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Recognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)  

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An efficient representation of 3D buildings: application to the evaluation of city models / Oussama Ennafii (2021)  

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Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)  

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Evaluating geo-tagged Twitter data to analyze tourist flows in Styria, Austria / Johannes Scholz in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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