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Characterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng 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 10 - 23 Titre : Characterizing urban land changes of 30 global megacities using nighttime light time series stacks Type de document : Article/Communication Auteurs : Qiming Zheng, Auteur ; Qihao Weng, Auteur ; Ke Wang, Auteur Année de publication : 2021 Article en page(s) : pp 10 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] aménagement foncier [Termes descripteurs IGN] analyse harmonique [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] éclairage public [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] image VIIRS [Termes descripteurs IGN] mégalopole [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Worldwide urbanization has brought about diverse types of urban land use and land cover (LULC) changes. The diversity of urban land changes, however, have been greatly under studied, since the major focus of past research has been on urban growth. In this study, we proposed a framework to characterize diverse urban land changes of 30 global megacities using monthly nighttime light time series from VIIRS data. First, we developed a Logistic-Harmonic model to fit VIIRS time series. Second, by leveraging the uniqueness of urban land change and nighttime light data, we incorporated temporal information of VIIRS time series and proposed a new classification scheme to produce monthly maps of built-up areas and to disentangle urban land changes into five categories. Third, we provided an in-depth analysis and comparison of urban land change patterns of the selected megacities. Results demonstrated that the Logistic-Harmonic model yielded a robust performance in fitting VIIRS time series. Temporal features based classification can not only significantly improve the mapping accuracy of built-up areas, especially for regions with heterogeneous built-up and non-built-up landscapes, but also promoted temporal consistency and classification efficiency. Urban land changes occurred in 51% of the built-up pixels of the megacities. Compared with urban growth, other types of urban land change, particularly land use intensification, contributed to an unexpectedly large proportion of the changes (83%). The findings of this study offer an insightful understanding on global urbanization processes in megacities, and evoke further investigation on the environmental and ecological implications of urban land changes. Numéro de notice : A2021-101 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.002 date de publication en ligne : 16/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.002 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96873 [article]

Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon / Tamer ElGharbawi 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 1 - 9 Titre : Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon Type de document : Article/Communication Auteurs : Tamer ElGharbawi, Auteur ; Fawzi Zarzoura, Auteur Année de publication : 2021 Article en page(s) : pp 1 - 9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] Beyrouth [Termes descripteurs IGN] catastrophe [Termes descripteurs IGN] corrélation [Termes descripteurs IGN] décorrélation [Termes descripteurs IGN] dommage matériel [Termes descripteurs IGN] étude d'impact [Termes descripteurs IGN] filtre passe-haut [Termes descripteurs IGN] image radar moirée Résumé : (auteur) Early well-coordinated response during unexpected catastrophes can define the near future of the stricken regions. Beirut city, Lebanon, was one of the unfortunate regions to endure the horrific ordeal of an unexpected explosion that caused thousands of human casualties, billions of dollars’ worth of property damage, and destroyed its main maritime entry point. In this paper, we identify damaged regions and classify their severity using a simple and robust SAR correlation technique. We employ phase coherence and amplitude correlation of a SAR stack to estimate pixels’ damage probability using hypothesis testing. We use a spatial phase filter applied in the frequency domain to improve the estimated coherence by removing the spatial decorrelation component of the total estimated coherence. Using this filter improved the coherence of nearly 44.2% of pixels identified with coherence less than 0.25 in our study area. The estimated damaged regions are presented and compared against a damage map issued by Advanced Rapid Imaging and Analysis (ARIA) which shows an average agreement of 68.3%. Also, a fine agreement was observed when compared to optical satellite images. Numéro de notice : A2021-100 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.00 date de publication en ligne : 15/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96871 [article]

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 descripteurs IGN] algorithme de superpixels [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] filtre de déchatoiement [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] ondelette [Termes descripteurs IGN] reconstruction [Termes descripteurs 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]

An integrated method for DEM simplification with terrain structural features and smooth morphology preserved / Wenhao Yu 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 273 - 295 Titre : An integrated method for DEM simplification with terrain structural features and smooth morphology preserved Type de document : Article/Communication Auteurs : Wenhao Yu, Auteur ; Yifan Zhang, Auteur ; Tinghua Ai, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 273 - 295 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes descripteurs IGN] algorithme de lissage [Termes descripteurs IGN] analyse structurelle [Termes descripteurs IGN] arête [Termes descripteurs IGN] carte géomorphologique [Termes descripteurs IGN] filtrage statistique [Termes descripteurs IGN] ligne caractéristique [Termes descripteurs IGN] limite de terrain [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] visualisation multiéchelle Résumé : (auteur) As a key focus of cartography and terrain analysis, the simplification of a digital elevation model (DEM) is used to preserve the pattern features of the terrain surface while suppressing its details over multiple scales. Statistical filtering and structural analysis methods are commonly used for this process. The structural analysis method performs well in identifying terrain structural edges, while it tends to discard the smooth morphology of a terrain surface. In addition, the filter that aims to reduce noise on a surface may over-smooth the terrain structural edges. Therefore, to preserve both the terrain structural edges and smooth morphology, we propose to combine the techniques of statistical filtering and structural analysis. Specifically, all the critical elevation points and structural edges are first detected from the DEM surface by using the structural analysis method. Then, the iterative guided normal filter is used to smooth the generalized DEM with the guidance of the structure of the original surface. After this process, the terrain structure is retained in the smooth surface of the DEM. The experimental results with a real-world dataset show that our method can inherit the merits of both structural analysis and statistical filter in preserving terrain features for multi-scale DEM representations. Numéro de notice : A2021-038 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1772479 date de publication en ligne : 29/05/2020 En ligne : https://doi.org/10.1080/13658816.2020.1772479 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96747 [article]

Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model / Yizhuo 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 227 - 249 Titre : Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model Type de document : Article/Communication Auteurs : Yizhuo Li, Auteur ; Teng Fei, Auteur ; Yingjing Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 227 - 249 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] comportement [Termes descripteurs IGN] détection de visage [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données environnementales [Termes descripteurs IGN] émotion [Termes descripteurs IGN] entropie [Termes descripteurs IGN] psychologie [Termes descripteurs IGN] reconnaissance faciale [Termes descripteurs IGN] sciences humaines [Termes descripteurs IGN] vision Résumé : (auteur) Human emotion is an intrinsic psychological state that is influenced by human thoughts and behaviours. Human emotion distribution has been regarded as an important part of emotional geography research. However, it is difficult to form a global scaled map reflecting human emotions at the same sampling density because various emotional sampling data are usually positive occurrences without absence data. In this study, a methodological framework for mapping the global geographic distribution of human emotion is proposed and applied, combining a species distribution model with physical environment factors. State-of-the-art affective computing technology is used to extract human emotions from facial expressions in Flickr photos. Various human emotions are considered as different species to form their ‘habitats’ and predict the suitability, termed as ‘Emotional Habitat’. To our knowledge, this framework is the first method to predict emotional distribution from an ecological perspective. Different geographic distributions of seven dimensional emotions are explored and depicted, and emotional diversity and abnormality are detected at the global scale. These results confirm the effectiveness of our framework and offer new insights to understand the relationship between human emotions and the physical environment. Moreover, our method facilitates further rigorous exploration in emotional geography and enriches its content. Numéro de notice : A2021-037 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1755040 date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.1080/13658816.2020.1755040 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96746 [article]

GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening / Hao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 172 (February 2021)  

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Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, Vol 172 (February 2021)  

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Uncertainties and errors in algorithms for elevation gradients / Dong Shi in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

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Aleatoric uncertainty estimation for dense stereo matching via CNN-based cost volume analysis / Max Mehltretter in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

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An improved approach based on terrain-dependent mathematical models for georeferencing pushbroom satellite images / Behrooz Moradi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

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Automated detection of lineaments express geological linear features of a tropical region using topographic fabric grain algorithm and the SRTM DEM / Samy Ismail Elmahdy in Geocarto international, vol 36 n° 1 ([01/01/2021])  

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Building extraction from Lidar data using statistical methods / Haval Abdul-Jabbar Sadeq in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

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Elevation models for reproducible evaluation of terrain representation / Patrick Kennelly in Cartography and Geographic Information Science, vol 48 n° 1 (January 2021)  

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Extraction of street pole-like objects based on plane filtering from mobile LiDAR data / Jingming Tu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

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Fusion of ground penetrating radar and laser scanning for infrastructure mapping / Dominik Merkle in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

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