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Estimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study / Mir Reza Ghaffari Razin in GPS solutions, Vol 24 n° 3 (July 2020)  

Public [article]  inGPS solutions > Vol 24 n° 3 (July 2020) Titre : Estimation of tropospheric wet refractivity using tomography method and artificial neural networks in Iranian case study Type de document : Article/Communication Auteurs : Mir Reza Ghaffari Razin, Auteur ; Behzad Voosoghi, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] erreur moyenne quadratique [Termes descripteurs IGN] erreur relative [Termes descripteurs IGN] Iran [Termes descripteurs IGN] réfraction atmosphérique [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] retard troposphérique [Termes descripteurs IGN] retard troposphérique zénithal [Termes descripteurs IGN] tomographie par GPS [Termes descripteurs IGN] vapeur d'eau [Termes descripteurs IGN] voxel Résumé : (auteur) Using the observations from local and regional GPS networks, the estimation of slant wet delays (SWDs) is possible for each line of sight between satellite and receiver. The observations of SWD are used to model horizontal and vertical variations of the wet refractivity in the atmosphere above the study area. This work is done using the tomography method. In tomography, the horizontal variations of tropospheric wet refractivity are modeled with the polynomial in degree and rank of 2 with latitude and longitude as variables. Also, altitude variations are modeled in the form of discrete layers with constant heights. The main innovation is to estimate the tropospheric parameters for each line of sight by the artificial neural networks (ANNs). The SWD obtained from GPS observations for the different signals at each station is compared with the SWD generated by the ANNs (SWDGPS–SWDANNs). The square of the difference between these two values is introduced as the cost function in the ANNs. To evaluate, we used observations from October 27 to 31, 2011. The availability of GPS and radiosonde data is the main reason for choosing this timeframe. The correlation coefficient, root mean square error (RMSE), and relative error allow for evaluation of the proposed model. The results were also compared with the results of the voxel-based troposphere tomography method. For a more detailed evaluation, four test stations are selected and ANN zenith wet delays (ZWDANN) are compared with the ZWDGPS. Observations of test stations are not used in the modeling step. The correlation coefficient in the testing step for TomoANN and Tomovoxel is 0.9006 and 0.8863, respectively. The mean RMSE at 5 days for TomoANN and Tomovoxel is calculated as 0.63 and 0.71 mm/km, respectively. Also, the average relative error at the four test stations for TomoANN is 15.37% and for Tomovoxel it is 19.69%. The results demonstrate the better capability of the proposed method in the modeling of the tropospheric wet refractivity in the region of Iran. Numéro de notice : A2020-238 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-00979-y date de publication en ligne : 10/04/2020 En ligne : https://doi.org/10.1007/s10291-020-00979-y Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94986 [article]

ALERT: adversarial learning with expert regularization using Tikhonov operator for missing band reconstruction / Litu Rout in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) Titre : ALERT: adversarial learning with expert regularization using Tikhonov operator for missing band reconstruction Type de document : Article/Communication Auteurs : Litu Rout, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] cohérence géométrique [Termes descripteurs IGN] correction d'image [Termes descripteurs IGN] dégradation d'image [Termes descripteurs IGN] pollution acoustique [Termes descripteurs IGN] qualité d'image [Termes descripteurs IGN] reconstruction [Termes descripteurs IGN] régularisation de Tychonoff Résumé : (auteur) The Earth observation using remote sensing is one of the most important technologies to assimilate key attributes about the Earth’s surface. To achieve tangible consequence, the internal building blocks of such a complex system must operate flawlessly. However, due to a dynamically changing environment, degradation in sensor electronics, and extreme weather condition remotely sensed images often miss essential information. As the sensors operate over several years in space the likelihood of sensor degradation persists. This results in commonly observed issues, such as stripe noise, missing partial data, and missing band. Various ground-based solutions have been developed to address these technological bottlenecks individually. In this article, we devise a method, which we call ALERT, to tackle missing band reconstruction. The proposed method reconstructs the missing band with the sole supervision of spectral and spatial priors. We compare the proposed framework with state-of-the-art methods and show compelling improvement both qualitatively and quantitatively. We provide both theoretical and empirical evidence of better performance by regularized adversarial learning as compared to complete supervision. Furthermore, we propose a new residual-dense-block (RDB) module to preserve geometric fidelity and assist in efficient gradient flow. We show that ALERT captures essential features such that the spatial and spectral characteristics of the reconstructed band remains preserved. To critically analyze the generalization we test the performance on two different satellite data sets: Resourcesat-2A and WorldView-2. As per our extensive experimentation, the proposed method achieves 20.72%, 13.81%, 1.05%, 15.91%, and 2.94% improvement in the root mean square error (RMSE), SAM, SSIM, PSNR, and SRE, respectively, over the state-of-the-art model. Numéro de notice : A2020-285 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2963818 date de publication en ligne : 16/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2963818 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95108 [article]

Counting of grapevine berries in images via semantic segmentation using convolutional neural networks / Laura Zabawa in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 73 - 83 Titre : Counting of grapevine berries in images via semantic segmentation using convolutional neural networks Type de document : Article/Communication Auteurs : Laura Zabawa, Auteur ; Anna Kicherer, Auteur ; Lasse Klingbeil, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 73 - 83 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] comptage [Termes descripteurs IGN] échantillon [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] extraction semi-automatique [Termes descripteurs IGN] régression [Termes descripteurs IGN] rendement agricole [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] traitement d'image [Termes descripteurs IGN] viticulture Résumé : (auteur) The extraction of phenotypic traits is often very time and labour intensive. Especially the investigation in viticulture is restricted to an on-site analysis due to the perennial nature of grapevine. Traditionally skilled experts examine small samples and extrapolate the results to a whole plot. Thereby different grapevine varieties and training systems, e.g. vertical shoot positioning (VSP) and semi minimal pruned hedges (SMPH) pose different challenges. In this paper we present an objective framework based on automatic image analysis which works on two different training systems. The images are collected semi automatic by a camera system which is installed in a modified grape harvester. The system produces overlapping images from the sides of the plants. Our framework uses a convolutional neural network to detect single berries in images by performing a semantic segmentation. Each berry is then counted with a connected component algorithm. We compare our results with the Mask-RCNN, a state-of-the-art network for instance segmentation and with a regression approach for counting. The experiments presented in this paper show that we are able to detect green berries in images despite of different training systems. We achieve an accuracy for the berry detection of 94.0% in the VSP and 85.6% in the SMPH. Numéro de notice : A2020-252 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.002 date de publication en ligne : 22/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.002 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94996 [article]

Ensemble learning for hyperspectral image classification using tangent collaborative representation / Hongjun Su in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 3778 - 3790 Titre : Ensemble learning for hyperspectral image classification using tangent collaborative representation Type de document : Article/Communication Auteurs : Hongjun Su, Auteur ; Yao Yu, Auteur ; Qian Du, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3778 - 3790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image numérique [Termes descripteurs IGN] boosting adapté [Termes descripteurs IGN] Bootstrap (statistique) [Termes descripteurs IGN] classification [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] conception collaborative [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] échantillon [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] neurone artificiel [Termes descripteurs IGN] performance [Termes descripteurs IGN] régression Résumé : (auteur) Recently, collaborative representation classification (CRC) has attracted much attention for hyperspectral image analysis. In particular, tangent space CRC (TCRC) has achieved excellent performance for hyperspectral image classification in a simplified tangent space. In this article, novel Bagging-based TCRC (TCRC-bagging) and Boosting-based TCRC (TCRC-boosting) methods are proposed. The main idea of TCRC-bagging is to generate diverse TCRC classification results using the bootstrap sample method, which can enhance the accuracy and diversity of a single classifier simultaneously. For TCRC-boosting, it can provide the most informative training samples by changing their distributions dynamically for each base TCRC learner. The effectiveness of the proposed methods is validated using three real hyperspectral data sets. The experimental results show that both TCRC-bagging and TCRC-boosting outperform their single classifier counterpart. In particular, the TCRC-boosting provides superior performance compared with the TCRC-bagging. Numéro de notice : 25280 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2957135 date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2957135 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95100 [article]

Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data / Shivangi Srivastava in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 6 (June 2020) . - pp 1117 - 1136 Titre : Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data Type de document : Article/Communication Auteurs : Shivangi Srivastava, Auteur ; John E. Vargas-Muñoz, Auteur ; Sylvain Lobry, Auteur ; Devis Tuia, Auteur Année de publication : 2020 Article en page(s) : pp 1117 - 1136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données urbaines [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] données localisées libres [Termes descripteurs IGN] Ile-de-France [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] image terrestre [Termes descripteurs IGN] information géographique [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] OpenStreetMap [Termes descripteurs IGN] réseau social Résumé : (auteur) We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deep-learning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization. Numéro de notice : A2020-269 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542698 date de publication en ligne : 18/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542698 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95041 [article]

Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland / Izabela Karsznia in Geocarto international, vol 35 n° 7 ([15/05/2020])  

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Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks / Mahmoud Saeedimoghaddam in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

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A convolutional neural network with mapping layers for hyperspectral image classification / Rui Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Exploring the potential of deep learning segmentation for mountain roads generalisation / Azelle Courtial in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)  

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Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Improved wavelet neural network based on change rate to predict satellite clock bias / Xu Wang in Survey review, vol 52 n° 372 (May 2020)  

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A review of assessment methods for cellular automata models of land-use change and urban growth / Xiaohua Tong in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

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Saliency-guided single shot multibox detector for target detection in SAR images / Lan Du in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Adaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation / Deliang Xiang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

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Automated terrain feature identification from remote sensing imagery: a deep learning approach / Wenwen Li in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

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