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Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks / Sina Mohammadi in ISPRS Journal of photogrammetry and remote sensing, vol 198 (April 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 198 (April 2023) . - pp 272 - 283 Titre : Improvement in crop mapping from satellite image time series by effectively supervising deep neural networks Type de document : Article/Communication Auteurs : Sina Mohammadi, Auteur ; Mariana Belgiu, Auteur ; Alfred Stein, Auteur Année de publication : 2023 Article en page(s) : pp 272 - 283 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage dirigé [Termes IGN] apprentissage profond [Termes IGN] carte de la végétation [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] cultures [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] série temporelle Résumé : (auteur) Deep learning methods have achieved promising results in crop mapping using satellite image time series. A challenge still remains on how to better learn discriminative feature representations to detect crop types when the model is applied to unseen data. To address this challenge and reveal the importance of proper supervision of deep neural networks in improving performance, we propose to supervise intermediate layers of a designed 3D Fully Convolutional Neural Network (FCN) by employing two middle supervision methods: Cross-entropy loss Middle Supervision (CE-MidS) and a novel middle supervision method, namely Supervised Contrastive loss Middle Supervision (SupCon-MidS). This method pulls together features belonging to the same class in embedding space, while pushing apart features from different classes. We demonstrate that SupCon-MidS enhances feature discrimination and clustering throughout the network, thereby improving the network performance. In addition, we employ two output supervision methods, namely F1 loss and Intersection Over Union (IOU) loss. Our experiments on identifying corn, soybean, and the class Other from Landsat image time series in the U.S. corn belt show that the best set-up of our method, namely IOU+SupCon-MidS, is able to outperform the state-of-the-art methods by scores of 3.5% and 0.5% on average when testing its accuracy across a different year (local test) and different regions (spatial test), respectively. Further, adding SupCon-MidS to the output supervision methods improves scores by 1.2% and 7.6% on average in local and spatial tests, respectively. We conclude that proper supervision of deep neural networks plays a significant role in improving crop mapping performance. The code and data are available at: https://github.com/Sina-Mohammadi/CropSupervision. Numéro de notice : A2023-203 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2023.03.007 Date de publication en ligne : 29/03/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.03.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103105 [article]

Spatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images / Bin Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 176 (June 2021) . - pp 222 - 236 Titre : Spatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images Type de document : Article/Communication Auteurs : Bin Zhang, Auteur ; Ling Chang, Auteur ; Alfred Stein, Auteur Année de publication : 2021 Article en page(s) : pp 222 - 236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] déformation de surface [Termes IGN] données spatiotemporelles [Termes IGN] image radar moirée [Termes IGN] image Radarsat [Termes IGN] méthode de Monte-Carlo [Termes IGN] points homologues [Termes IGN] série temporelle Résumé : (auteur) A recent development in Interferometric Synthetic Aperture Radar (InSAR) technology is integrating multiple SAR satellite data to dynamically extract ground features. This paper addresses two relevant challenges: identification of common ground targets from different SAR datasets in space, and concatenation of time series when dealing with temporal dynamics. To address the first challenge, we describe the geolocation uncertainty of InSAR measurements as a three-dimensional error ellipsoid. The points, among InSAR measurements, which have error ellipsoids with a positive cross volume are identified as tie-point pairs representing common ground objects from multiple SAR datasets. The cross volumes are calculated using Monte Carlo methods and serve as weights to achieve the equivalent deformation time series. To address the second challenge, the deformation time series model for each tie-point pair is estimated using probabilistic methods, where potential deformation models are efficiently tested and evaluated. As an application, we integrated two Radarsat-2 datasets in Standard and Extra-Fine modes to map the subsidence of the west of the Netherlands between 2010 and 2017. We identified 18128 tie-point pairs, 5 intersection types of error ellipsoids, 5 deformation models, and constructed their long-term deformation time series. The detected maximum mean subsidence velocity in Line-Of-Sight direction is up to 15 . We conclude that our method removes limitations that exist in single-viewing-geometry SAR when integrating multiple SAR data. In particular, the proposed time-series modeling method is useful to achieve a long-term deformation time series of multiple datasets. Numéro de notice : A2021-414 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.005 Date de publication en ligne : 08/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97745 [article]

Predictive land value modelling in Guatemala City using a geostatistical approach and Space Syntax / Jose Morales in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 7 (July 2020) . - pp 1451 - 1474 Titre : Predictive land value modelling in Guatemala City using a geostatistical approach and Space Syntax Type de document : Article/Communication Auteurs : Jose Morales, Auteur ; Alfred Stein, Auteur ; Johannes Flacke, Auteur ; Jaap Zevenbergen, Auteur Année de publication : 2020 Article en page(s) : pp 1451 - 1474 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de la valeur [Termes IGN] analyse syntaxique [Termes IGN] cartographie statistique [Termes IGN] estimation quantitative [Termes IGN] évaluation foncière [Termes IGN] géostatistique [Termes IGN] Guatemala [Termes IGN] krigeage [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] modèle conceptuel de données localisées [Termes IGN] modèle de simulation [Termes IGN] régression [Termes IGN] système d'information foncière Résumé : (auteur) Spatial information of land values is fundamental for planners and policy makers. Individual appraisals are costly, explaining the need for predictive modelling. Recent work has investigated using Space Syntax to analyse urban access and explain land values. However, the spatial dependence of urban land markets has not been addressed in such studies. Further, the selection of meaningful variables is commonly conducted under non-spatialized modelling conditions. The objective of this paper is to construct a land value map using a geostatistical approach using Space Syntax and a spatialized variable selection. The methodology is applied in Guatemala City. We used an existing dataset of residential land value appraisals and accessibility metrics. Regression-kriging was used to conduct variable selection and derive a model for spatial prediction. The prediction accuracy is compared with a multivariate regression. The results show that a spatialized variable selection yields a more parsimonious model with higher prediction accuracy. New insights were found on how Space Syntax explains land value variability when also modelling the spatial dependence. Space Syntax can contribute with relevant spatialized information for predictive land value modelling purposes. Finally, the spatial modelling framework facilitates the production of spatial information of land values that is relevant for planning practice. Numéro de notice : A2020-306 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1725014 Date de publication en ligne : 11/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1725014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95148 [article]

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A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration / Milad Mahour in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 126 (April 2017) . - pp 56 – 67 Titre : A comparison of two downscaling procedures to increase the spatial resolution of mapping actual evapotranspiration Type de document : Article/Communication Auteurs : Milad Mahour, Auteur ; Valentyn Tolpekin, Auteur ; Alfred Stein, Auteur ; Ali Sharifi, Auteur Année de publication : 2017 Article en page(s) : pp 56 – 67 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] évapotranspiration [Termes IGN] image à moyenne résolution [Termes IGN] image Aqua-MODIS [Termes IGN] image Landsat-8 [Termes IGN] image Terra-MODIS [Termes IGN] Iran [Termes IGN] irrigation [Termes IGN] krigeage [Termes IGN] mise à l'échelle [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] pouvoir de résolution géométrique [Termes IGN] température au sol Résumé : (auteur) This research addressed the effects of downscaling cokriging Land Surface Temperature (LST) on estimation of Actual Evapotranspiration (AET) from remote sensing images. Two procedures were followed. We first applied downscaling cokriging to a coarse resolution LST product of MODIS at 1000 m. With its outcome, daily AET of a medium spatial resolution (250 m) was obtained using the Surface Energy Balance System (SEBS). Second, we downscaled a coarse AET map to medium spatial resolution (250 m). For both procedures, the 250 m resolution MODIS NDVI product was used as a co-variable. Validation was carried out using Landsat 8 images, from which LST was derived from the thermal bands. The two procedures were applied to an agricultural area with a traditional irrigation network in Iran. We obtained an average LST value of 305.8 K as compared to a downscaled LST value of 307.0 K. Reference AET estimated with SEBS using Landsat 8 data was equal to 5.756 mm day−1, as compared with a downscaled AET value of 5.571 mm day−1. The RMSE between reference AET and downscaled AET was equal to 1.26 mm day−1 (r = 0.49) and between reference and downscaled LST to 3.67 K (r = 0.48). The study showed that AET values obtained with the two downscaling procedures were similar to each other, but that AET showed a higher spatial variability if obtained with downscaled LST. We concluded that LST had a large effect on producing AET maps from Remote Sensing (RS) images, and that downscaling cokriging was helpful to provide daily AET maps at medium spatial resolution. Numéro de notice : A2017-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.02.004 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2017.02.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84508 [article]

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Urban slum detection using texture and spatial metrics derived from satellite imagery / Divyani Kohli in Journal of spatial science, vol 61 n° 2 (December 2016)  

Public [article]  inJournal of spatial science > vol 61 n° 2 (December 2016) . - pp 405 - 426 Titre : Urban slum detection using texture and spatial metrics derived from satellite imagery Type de document : Article/Communication Auteurs : Divyani Kohli, Auteur ; Robert Sliuzas, Auteur ; Alfred Stein, Auteur Année de publication : 2016 Article en page(s) : pp 405 - 426 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] détection du bâti [Termes IGN] image Quickbird [Termes IGN] Maharashtra (Inde ; état) [Termes IGN] ontologie [Termes IGN] spatial metrics [Termes IGN] texture d'image [Termes IGN] ville Résumé : (auteur) Slum detection from satellite imagery is challenging due to the variability in slum types and definitions. This research aimed at developing a method for slum detection based on the morphology of the built environment. The method consists of segmentation followed by hierarchical classification using object-oriented image analysis and integrating expert knowledge in the form of a local slum ontology. Results show that textural feature contrast derived from a grey-level co-occurrence matrix was useful for delineating segments of slum areas or parts thereof. Spatial metrics such as the size of segments and proportions of vegetation and built-up were used for slum detection. The percentage of agreement between the reference layer and slum classification was 60 percent. This is lower than the accuracy achieved for land cover classification (80.8 percent), due to large variations. We conclude that the method produces useful results and has potential for successful application in contexts with similar morphology. Numéro de notice : A2016--147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/14498596.2016.1138247 Date de publication en ligne : 05/05/2016 En ligne : http://dx.doi.org/10.1080/14498596.2016.1138247 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86334 [article]

Identification and utilization of land-use type importance for land-use data generalization / Wenxiu Gao in Cartographic journal (the), Vol 53 n° 1 (February 2016)  

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Uncertainty modelling and quality control for spatial data / Wenzhong Shi (2016)

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Using geographically weighted regression kriging for crop yield mapping in West Africa / Muhammad Imran in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)  

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A random set approach for modeling integrated uncertainties of traffic islands derived from airborne laser scanning points / Liang Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 9 (September 2013)  

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Improving representation of land-use maps derived from object-oriented image classification / Wenxiu Gao in Transactions in GIS, vol 17 n° 3 (June 2013)  

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vol 66 n° 2 - March - April 2011 - Quality, scale and analysis aspects of urban city models (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Monika Sester  

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A system of types and operators for handling vague spatial objects / A. Dilo in International journal of geographical information science IJGIS, vol 21 n° 3-4 (march - april 2007)  

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Modeling and visualizing dynamic landscape objects and their qualities / Daniel van de Vlag (2006)  

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An application of problem and product ontologies for the revision beach nourishments / Daniel van de Vlag in International journal of geographical information science IJGIS, vol 19 n° 10 (november 2005)  

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Complexity metrics to quantify semantic accuracy in segmented Landsat images / Alfred Stein in International Journal of Remote Sensing IJRS, vol 26 n° 14 (July 2005)  

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