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Robust unsupervised small area change detection from SAR imagery using deep learning / Xinzheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 173 (March 2021) . - pp 79 - 94 Titre : Robust unsupervised small area change detection from SAR imagery using deep learning Type de document : Article/Communication Auteurs : Xinzheng Zhang, Auteur ; Hang Su, Auteur ; Ce Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 79 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] algorithme de superpixels [Termes IGN] apprentissage profond [Termes IGN] classification floue [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de changement [Termes IGN] échantillonnage [Termes IGN] filtre de déchatoiement [Termes IGN] image radar moirée [Termes IGN] ondelette [Termes IGN] regroupement de données Résumé : (auteur) Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection. Numéro de notice : A2021-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.004 date de publication en ligne : 17/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96879 [article]

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Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6039 - 6058 Titre : Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis Type de document : Article/Communication Auteurs : David Solarna, Auteur ; Alberto Gotelli, Auteur ; Jacqueline Le Moigne, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6039 - 6058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] cratère [Termes IGN] détection de contours [Termes IGN] distance de Hausdorff [Termes IGN] extraction de traits caractéristiques [Termes IGN] image multitemporelle [Termes IGN] image thermique [Termes IGN] Mars (planète) [Termes IGN] ondelette [Termes IGN] processus ponctuel marqué [Termes IGN] séparateur à vaste marge [Termes IGN] transformation de Hough [Termes IGN] zone d'intérêt Résumé : (auteur) Because of the large variety of planetary sensors and spacecraft already collecting data and with many new and improved sensors being planned for future missions, planetary science needs to integrate numerous multimodal image sources, and, as a consequence, accurate and robust registration algorithms are required. In this article, we develop a new framework for crater detection based on marked point processes (MPPs) that can be used for planetary image registration. MPPs were found to be effective for various object detection tasks in Earth observation, and a new MPP model is proposed here for detecting craters in planetary data. The resulting spatial features are exploited for registration, together with fitness functions based on the MPP energy, on the mean directed Hausdorff distance, and on the mutual information. Two different methods—one based on birth–death processes and region-of-interest analysis and the other based on graph cuts and decimated wavelets—are developed within the proposed framework. Experiments with a large set of images, including 13 thermal infrared and visible images of the Mars surface, 20 semisimulated multitemporal pairs of images of the Mars surface, and a real multitemporal image pair of the Lunar surface, demonstrate the effectiveness of the proposed framework in terms of crater detection performance as well as for subpixel registration accuracy. Numéro de notice : A2020-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2970908 date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2970908 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95704 [article]

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

Public [article]  inSurvey review > vol 52 n° 372 (May 2020) Titre : Improved wavelet neural network based on change rate to predict satellite clock bias Type de document : Article/Communication Auteurs : Xu Wang, Auteur ; Hongzhou Chai, Auteur ; Chang Wang, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] courbe de Gauss [Termes IGN] erreur systématique interfréquence d'horloge [Termes IGN] estimation de précision [Termes IGN] ondelette [Termes IGN] ondelette de Shannon [Termes IGN] prévision [Termes IGN] réseau neuronal artificiel Résumé : (auteur) To develop a high-accuracy method for predicting SCB based on the analysis of the shortcomings of the wavelet neural network (WNN) model, an improved WNN model to predict SCB is proposed herein. The activation function of the WNN is constructed by combining the advantages of Shannon and Gauss ‘window’ functions to improve the WNN. Finally, the improved WNN model is used to predict SCB. The results show that the proposed model has the highest prediction accuracy, stability, and robustness. Moreover, it effectively predicts long-time SCB data. Therefore, the proposed model can predict SCB with high accuracy. Numéro de notice : A2020-289 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1758999 date de publication en ligne : 24/05/2020 En ligne : https://doi.org/10.1080/00396265.2020.1758999 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95117 [article]

Evaluation of the high-rate GNSS-PPP method for vertical structural motion / Mosbeh R. Kaloop in Survey review, vol 52 n° 371 (March 2020)  

Public [article]  inSurvey review > vol 52 n° 371 (March 2020) . - pp 159 - 171 Titre : Evaluation of the high-rate GNSS-PPP method for vertical structural motion Type de document : Article/Communication Auteurs : Mosbeh R. Kaloop, Auteur ; Cemal Ozer Yigit, Auteur ; Ahmet Anil Dindar, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 159 - 171 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] antenne GNSS [Termes IGN] déformation verticale de la croute terrestre [Termes IGN] filtrage du bruit [Termes IGN] ondelette [Termes IGN] positionnement cinématique en temps réel [Termes IGN] positionnement par GNSS [Termes IGN] positionnement ponctuel précis [Termes IGN] série temporelle [Termes IGN] traitement de données GNSS Résumé : (auteur) This study aims at the investigation of GNSS-PP method to determine the dynamic characteristics of structures. Cantilever steel bars having lengths of 70, 100 and 120 cm were tested under dynamic excitation. The GNSS was used to measure the natural frequencies and damping values of all the tested cantilever structures. The GNSS data were processed using relative GNSS positioning and PPP methods. The results obtained using these two methods were also compared with the dynamic characteristics obtained by applying the theoretical and finite element (FE) methods. Furthermore, it is investigated the impact of the stable data length before oscillation events on kinematic PPP. The study showed that the maximum difference among the experimental results in terms of natural frequencies proceeded using PPP is 0.08 Hz when compared with the theoretical and FE results. Furthermore, there is no difference between the PPP and relative GNSS positioning in determining the dynamic behaviour of structures eventhough roving GNSS antenna remains motionless for short-time, such as a few-minutes, before an event occurred. Numéro de notice : A2020-080 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1534362 date de publication en ligne : 19/10/2018 En ligne : https://doi.org/https://doi.org/10.1080/00396265.2018.1534362 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94643 [article]

INS/GNSS integration using recurrent fuzzy wavelet neural networks / Parisa Doostdar in GPS solutions, vol 24 n° 1 (January 2020)  

Public [article]  inGPS solutions > vol 24 n° 1 (January 2020) Titre : INS/GNSS integration using recurrent fuzzy wavelet neural networks Type de document : Article/Communication Auteurs : Parisa Doostdar, Auteur ; Jafar Keighobadi, Auteur ; Mohammad Ali Hamed, Auteur Année de publication : 2020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification floue [Termes IGN] couplage GNSS-INS [Termes IGN] données GNSS [Termes IGN] filtre de Kalman [Termes IGN] interruption du signal [Termes IGN] ondelette [Termes IGN] réseau neuronal artificiel [Termes IGN] réseau neuronal récurrent [Termes IGN] vitesse [Vedettes matières IGN] Traitement de données GNSS Résumé : (Auteur) In recent years, aided navigation systems through combining inertial navigation system (INS) with global navigation satellite system (GNSS) have been widely applied to enhance the position, velocity, and attitude information of autonomous vehicles. In order to gain the accuracy of the aided INS/GNSS in GNSS gap intervals, a heuristic neural network structure based on the recurrent fuzzy wavelet neural network (RFWNN) is applicable for INS velocity and position error compensation purpose. During frequent access to GNSS data, the RFWNN should be trained as a highly precise prediction model equipped with the Kalman filter algorithm. Therefore, the INS velocity and position error data are obtainable along with the lost intervals of GNSS signals. For performance assessment of the proposed RFWNN-aided INS/GNSS, real flight test data of a small commercial unmanned aerial vehicle (UAV) were conducted. A comparison of test results shows that the proposed NN algorithm could efficiently provide high-accuracy corrections on the INS velocity and position information during GNSS outages. Numéro de notice : A2020-019 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0942-z date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1007/s10291-019-0942-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94458 [article]

Geospatial data organization methods with emphasis on aperture-3 hexagonal discrete global grid systems / Ali Mahdavi Amiri in Cartographica, vol 54 n° 1 (spring 2019)  

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Contributions to SAR image time series analysis / Ammar Mian (2019)  

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Méthodes d'apprentissage statistique pour la détection de la signalisation routière à partir de véhicules traceurs / Yann Méneroux (2019)  

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Denoising of natural images through robust wavelet thresholding and genetic programming / Asem Khmag in The Visual Computer, vol 33 n°9 (September 2017)  

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WREP : A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops / Dong Li in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)  

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Multi-scale modeling of Earth's gravity field in space and time / Shuo Wang in Journal of geodynamics, vol 106 (May 2017)  

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Evaluation of pan-sharpening methods for spatial and spectral quality / Jagalingam Pushparaj in Applied geomatics, vol 9 n° 1 (March 2017)  

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Gravity forward modeling with a tesseroid-based rock-water-ice approach / Thomas Grombein (2017)    

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Ondelettes et processus stochastiques / Abdourrahmane M. Atto (2017)

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Télédétection pour l'observation des surfaces continentales, ch. 6. Méthodes de traitement de données lidar / Clément Mallet (2017)

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