Optimization of Optical Image Geometric Modeling, Application to Topography Extraction and Topographic Change Measurements Using PlanetScope and SkySat Imagery

The volume of data generated by earth observation satellites has increased tremendously over the last few decades and will increase further in the coming decade thanks in particular to the launch of nanosatellites constellations. These data should open new avenues for Earth surface monitoring due to highly improved spectral, spatial and temporal resolution. Many applications depend, however, on the accuracy of the image geometric model. The geometry of optical images, whether acquired from pushbroom or frame systems, is now commonly represented using a Rational Function Model (RFM). While the formalism has become standard, the procedures used to generate these models and their accuracies are diverse. As a result, the RFM models delivered with commercial data are commonly not accurate enough for 3-D extraction, subpixel registration or ground deformation measurements. In this study, we present a methodology for RFM optimization and demonstrate its potential for 3D reconstruction using tri-stereo and multi-date Cubesat images provided by SkySat and PlanetScope, respectively. We use SkySat data over the Morenci Mine, Arizona, which is the largest copper mine in the United States. The re-projection error after the RFM refinement is 0.42 pix without using ground control points (GCPs). Comparison of our Digital Elevation Model (DEM with ~3 m GSD) with a reference DEM obtained from an airborne LiDAR survey (with ~1 m GSD) over stable areas yields a standard deviation of the elevation differences of ~3.9 m. The comparison of the two DEMs allows detecting and measuring the topographic changes due to the mine activity (excavation and stockpiles). We assess the potential of PlanetScope data, using multi-date DOVE-C images from the Shisper glacier, located in the Karakoram (Pakistan), which is known for its recent surge. We extracted DEMs in 2017 and 2019 before and after the surge. The re-projection error after the RFM refinement is 0.38 pix without using GCPs. The accuracy of our DEMs (with ~9 m GSD) is evaluated through comparison with the SRTM DEM (GSD ~30 m) and with a DEM (GSD ~2 m) calculated from Geoeye-1 (GE-1) and World-View-2 (WV-2) stereo images. The standard deviation of the elevation differences in stable areas between the PlanetScope DEM and SRTM is ~12 m, and ~7 m with the GE-1&WV-2 DEM. The mass transfer due to the surge is clearly revealed from a comparison of the 2017 and 2019 DEMs. The study demonstrates that, with the proposed scheme for RFM optimization, times series of DEM extracted from SkySat and PlanetScope images can be used to measure topographic changes due to mining activities or ice flow, and could also be used to monitor geomorphic processes such as landslides, or coastal erosion for example

Optimization of Optical Image Geometric Modeling, Application to Topography Extraction and Topographic Change Measurements Using PlanetScope and SkySat Imagery

The volume of data generated by earth observation satellites has increased tremendously over the last few decades and will increase further in the coming decade thanks in particular to the launch of nanosatellites constellations. These data should open new avenues for Earth surface monitoring due to highly improved spectral, spatial and temporal resolution. Many applications depend, however, on the accuracy of the image geometric model. The geometry of optical images, whether acquired from pushbroom or frame systems, is now commonly represented using a Rational Function Model (RFM). While the formalism has become standard, the procedures used to generate these models and their accuracies are diverse. As a result, the RFM models delivered with commercial data are commonly not accurate enough for 3-D extraction, subpixel registration or ground deformation measurements. In this study, we present a methodology for RFM optimization and demonstrate its potential for 3D reconstruction using tri-stereo and multi-date Cubesat images provided by SkySat and PlanetScope, respectively. We use SkySat data over the Morenci Mine, Arizona, which is the largest copper mine in the United States. The re-projection error after the RFM refinement is 0.42 pix without using ground control points (GCPs). Comparison of our Digital Elevation Model (DEM with ~3 m GSD) with a reference DEM obtained from an airborne LiDAR survey (with ~1 m GSD) over stable areas yields a standard deviation of the elevation differences of ~3.9 m. The comparison of the two DEMs allows detecting and measuring the topographic changes due to the mine activity (excavation and stockpiles). We assess the potential of PlanetScope data, using multi-date DOVE-C images from the Shisper glacier, located in the Karakoram (Pakistan), which is known for its recent surge. We extracted DEMs in 2017 and 2019 before and after the surge. The re-projection error after the RFM refinement is 0.38 pix without using GCPs. The accuracy of our DEMs (with ~9 m GSD) is evaluated through comparison with the SRTM DEM (GSD ~30 m) and with a DEM (GSD ~2 m) calculated from Geoeye-1 (GE-1) and World-View-2 (WV-2) stereo images. The standard deviation of the elevation differences in stable areas between the PlanetScope DEM and SRTM is ~12 m, and ~7 m with the GE-1&WV-2 DEM. The mass transfer due to the surge is clearly revealed from a comparison of the 2017 and 2019 DEMs. The study demonstrates that, with the proposed scheme for RFM optimization, times series of DEM extracted from SkySat and PlanetScope images can be used to measure topographic changes due to mining activities or ice flow, and could also be used to monitor geomorphic processes such as landslides, or coastal erosion for example

Contributions méthodologiques à la surveillance des changements de la surface terrestre en 2-D et 3D à l'aide de séries temporelles d'images optiques satellitaires multiplateformes

One of the emerging tools for monitoring Earth surface changes is the use of optical Earth observation (EO) satellite images. The last decades have seen an exponential growth of optical satellites with various characteristics such as acquisition mode, viewing geometry, radiometric, spatial, and temporal resolutions. One of the reasons for this growth is the use of the CubeSat concept for optical remote sensing. The increasing data volume enhanced the capability to acquire multitemporal data of the Earth surface with improved spectral, spatial, and temporal resolution. Consequently, this flexibility should enhance our ability to monitor geomorphological processes (e.g., landslides, coastal erosion, Aeolian processes), ground deformation due to earthquakes or landslides, mountain glaciers, and ice caps disasters damages, and human activities (e.g., urbanization, infrastructure development, mining operations). Therefore, in order to better monitor land surface changes, data from different sensors need to be leveraged by combining them into a single, coherent data stream. To ensure this harmonization, which is a major challenge especially from a geometric point of view, first and foremost, a precise co-registration of the images is required. In this context, this thesis provides a methodological contribution to mitigate geometry-related issues in order to monitor Earth Surface changes in 2-D and 3D using multi-platform time series of satellite optical images.In this thesis, the focus is first on identifying the main geometric artifacts in the provided optical satellite imaging products, whether orthorectified or not, or whether push-broom or push-frame. Second, t is demonstrated that when a large volume of data is available, it is possible to separate the surface deformation signal from these artifacts. Third, for non-orthorectified images, a procedure to optimize the registration of optical images delivered in the Rational Function Model (RFM) standard as well as using the Rigorous Sensor Model is developed. The method is sensor agnostic, thus allowing the joint co-registration of images from different platforms and producing geometrically consistent harmonized data. Forth, the potential of CubeSat imagery for time series analysis and monitoring the ground surface deformation in 2D and 3D is explored by proposing a method for geometric calibration, data curation, and digital elevation model (DEM) generation. Finally, a new method for resolving the full 3D surface deformation field using a sub-pixel image correlation technique and a ray-tracing approach independent of the input DEM and satellite viewing geometry is proposed.The developed methods are applied and validated to monitor different earth surface changes: earthquake (Ridgecrest, California), glaciology (Shisper surge, Pakistan), and mine activities (Morenci, Arizona). In addition, all the proposed methods are implemented into an open-source geospatial software referred to as Cosi-Corr+ (geoCosiCorr3D) and available from the Caltech Tectonics Observatory websiteL'un des outils émergents pour surveiller les changements à la surface de la Terre est l'utilisation d'images satellitaires pour l'observation de la Terre. Au cours de la dernière décennie, on a assisté à une croissance exponentielle de satellites optiques dotés de diverses caractéristiques, comme le mode d'acquisition, la géométrie d’acquisition, les résolutions radiométriques, spatiales et temporelles. L'une des raisons de cette croissance est l'utilisation du concept CubeSat. L'augmentation du volume de données a renforcé la capacité d'acquérir des données multi-temporelles de la surface de la Terre avec une meilleure résolution spectrale, spatiale et temporelle. Cette flexibilité devrait améliorer notre capacité à surveiller les processus géomorphologiques (par exemple, les glissements de terrain, l'érosion côtière, les processus éoliens), la déformation du sol due aux tremblements de terre ou aux glissements de terrain, les dommages causés par les glaciers de montagne et les calottes glaciaires, ainsi que les activités humaines (par exemple, l'urbanisation, le développement des infrastructures, les opérations minières). Par conséquent, afin de mieux surveiller ces changements, les données provenant de différents systèmes d’acquisitions doivent être exploitées en les combinant en un flux de données unique et cohérent. Afin d’assurer cette harmonisation, qui est un défi majeur surtout d'un point de vue géométrique, il faut avant assurer une bonne co-registration des images. Dans ce contexte, cette thèse fournit une contribution méthodologique pour atténuer les problèmes liés à la géométrie afin de surveiller les changements de la surface terrestre en 2D et 3D en utilisant des séries temporelles multiplateformes d'images optiques satellitaires.Dans cette thèse, on se concentre d'abord sur l'identification des principaux artefacts géométriques dans les produits d'imagerie satellitaire optique fournis, qu'ils soient orthorectifiés ou non, ou qu'ils soient push-broom ou push-frame. Deuxièmement, il est démontré que lorsqu'un grand volume de données est disponible, il est possible de séparer le signal de déformation de surface de ces artefacts. Troisièmement, pour les images non orthorectifiées, une procédure permettant d'optimiser le recalage des images optiques fournies avec model mathématique (Rational Function Model) ainsi qu'à l'aide des donnes physique (Rigorous Sensor Model) est développée. La méthode est agnostique par rapport aux capteurs, ce qui permet la co-registration conjointe d'images provenant de différentes plateformes et produit des données harmonisées géométriquement cohérentes. Ensuite, le potentiel de l'imagerie CubeSat pour l'analyse de séries temporelles et la surveillance de la déformation de la surface du sol en 2D et 3D est exploré en proposant une méthode de calibration géométrique, d’orthorectification et de génération de modèles numériques d'élévation (MNE). Enfin, une nouvelle méthode de résolution du champ complet de déformation de la surface en 3D utilisant une technique de corrélation d'images sous-pixel et une approche de traçage de rayons indépendante du MNE et de la géométrie de d’acquisition du satellite est proposée.Les méthodes développées sont appliquées et validées pour surveiller différents changements de la surface terrestre : tremblement de terre (Ridgecrest, Californie), glaciologie ( Shisper, Pakistan), et activités minières (Morenci, Arizona). En outre, toutes les méthodes proposées sont mises en œuvre dans un logiciel géospatial libre appelé Cosi-Corr+ (geoCosiCorr3D) et disponible sur le site Web du Caltech Tectonics Observator

Contributions méthodologiques à la surveillance des changements de la surface terrestre en 2-D et 3D à l'aide de séries temporelles d'images optiques satellitaires multiplateformes

L'un des outils émergents pour surveiller les changements à la surface de la Terre est l'utilisation d'images satellitaires pour l'observation de la Terre. Au cours de la dernière décennie, on a assisté à une croissance exponentielle de satellites optiques dotés de diverses caractéristiques, comme le mode d'acquisition, la géométrie d’acquisition, les résolutions radiométriques, spatiales et temporelles. L'une des raisons de cette croissance est l'utilisation du concept CubeSat. L'augmentation du volume de données a renforcé la capacité d'acquérir des données multi-temporelles de la surface de la Terre avec une meilleure résolution spectrale, spatiale et temporelle. Cette flexibilité devrait améliorer notre capacité à surveiller les processus géomorphologiques (par exemple, les glissements de terrain, l'érosion côtière, les processus éoliens), la déformation du sol due aux tremblements de terre ou aux glissements de terrain, les dommages causés par les glaciers de montagne et les calottes glaciaires, ainsi que les activités humaines (par exemple, l'urbanisation, le développement des infrastructures, les opérations minières). Par conséquent, afin de mieux surveiller ces changements, les données provenant de différents systèmes d’acquisitions doivent être exploitées en les combinant en un flux de données unique et cohérent. Afin d’assurer cette harmonisation, qui est un défi majeur surtout d'un point de vue géométrique, il faut avant assurer une bonne co-registration des images. Dans ce contexte, cette thèse fournit une contribution méthodologique pour atténuer les problèmes liés à la géométrie afin de surveiller les changements de la surface terrestre en 2D et 3D en utilisant des séries temporelles multiplateformes d'images optiques satellitaires.Dans cette thèse, on se concentre d'abord sur l'identification des principaux artefacts géométriques dans les produits d'imagerie satellitaire optique fournis, qu'ils soient orthorectifiés ou non, ou qu'ils soient push-broom ou push-frame. Deuxièmement, il est démontré que lorsqu'un grand volume de données est disponible, il est possible de séparer le signal de déformation de surface de ces artefacts. Troisièmement, pour les images non orthorectifiées, une procédure permettant d'optimiser le recalage des images optiques fournies avec model mathématique (Rational Function Model) ainsi qu'à l'aide des donnes physique (Rigorous Sensor Model) est développée. La méthode est agnostique par rapport aux capteurs, ce qui permet la co-registration conjointe d'images provenant de différentes plateformes et produit des données harmonisées géométriquement cohérentes. Ensuite, le potentiel de l'imagerie CubeSat pour l'analyse de séries temporelles et la surveillance de la déformation de la surface du sol en 2D et 3D est exploré en proposant une méthode de calibration géométrique, d’orthorectification et de génération de modèles numériques d'élévation (MNE). Enfin, une nouvelle méthode de résolution du champ complet de déformation de la surface en 3D utilisant une technique de corrélation d'images sous-pixel et une approche de traçage de rayons indépendante du MNE et de la géométrie de d’acquisition du satellite est proposée.Les méthodes développées sont appliquées et validées pour surveiller différents changements de la surface terrestre : tremblement de terre (Ridgecrest, Californie), glaciologie ( Shisper, Pakistan), et activités minières (Morenci, Arizona). En outre, toutes les méthodes proposées sont mises en œuvre dans un logiciel géospatial libre appelé Cosi-Corr+ (geoCosiCorr3D) et disponible sur le site Web du Caltech Tectonics ObservatoryOne of the emerging tools for monitoring Earth surface changes is the use of optical Earth observation (EO) satellite images. The last decades have seen an exponential growth of optical satellites with various characteristics such as acquisition mode, viewing geometry, radiometric, spatial, and temporal resolutions. One of the reasons for this growth is the use of the CubeSat concept for optical remote sensing. The increasing data volume enhanced the capability to acquire multitemporal data of the Earth surface with improved spectral, spatial, and temporal resolution. Consequently, this flexibility should enhance our ability to monitor geomorphological processes (e.g., landslides, coastal erosion, Aeolian processes), ground deformation due to earthquakes or landslides, mountain glaciers, and ice caps disasters damages, and human activities (e.g., urbanization, infrastructure development, mining operations). Therefore, in order to better monitor land surface changes, data from different sensors need to be leveraged by combining them into a single, coherent data stream. To ensure this harmonization, which is a major challenge especially from a geometric point of view, first and foremost, a precise co-registration of the images is required. In this context, this thesis provides a methodological contribution to mitigate geometry-related issues in order to monitor Earth Surface changes in 2-D and 3D using multi-platform time series of satellite optical images.In this thesis, the focus is first on identifying the main geometric artifacts in the provided optical satellite imaging products, whether orthorectified or not, or whether push-broom or push-frame. Second, t is demonstrated that when a large volume of data is available, it is possible to separate the surface deformation signal from these artifacts. Third, for non-orthorectified images, a procedure to optimize the registration of optical images delivered in the Rational Function Model (RFM) standard as well as using the Rigorous Sensor Model is developed. The method is sensor agnostic, thus allowing the joint co-registration of images from different platforms and producing geometrically consistent harmonized data. Forth, the potential of CubeSat imagery for time series analysis and monitoring the ground surface deformation in 2D and 3D is explored by proposing a method for geometric calibration, data curation, and digital elevation model (DEM) generation. Finally, a new method for resolving the full 3D surface deformation field using a sub-pixel image correlation technique and a ray-tracing approach independent of the input DEM and satellite viewing geometry is proposed.The developed methods are applied and validated to monitor different earth surface changes: earthquake (Ridgecrest, California), glaciology (Shisper surge, Pakistan), and mine activities (Morenci, Arizona). In addition, all the proposed methods are implemented into an open-source geospatial software referred to as Cosi-Corr+ (geoCosiCorr3D) and available from the Caltech Tectonics Observatory websit

Optimization of Optical Image Geometric Modeling, Application to Topography Extraction and Topographic Change Measurements Using PlanetScope and SkySat Imagery

The volume of data generated by earth observation satellites has increased tremendously over the last few decades and will increase further in the coming decade thanks in particular to the launch of nanosatellites constellations. These data should open new avenues for Earth surface monitoring due to highly improved spectral, spatial and temporal resolution. Many applications depend, however, on the accuracy of the image geometric model. The geometry of optical images, whether acquired from pushbroom or frame systems, is now commonly represented using a Rational Function Model (RFM). While the formalism has become standard, the procedures used to generate these models and their accuracies are diverse. As a result, the RFM models delivered with commercial data are commonly not accurate enough for 3-D extraction, subpixel registration or ground deformation measurements. In this study, we present a methodology for RFM optimization and demonstrate its potential for 3D reconstruction using tri-stereo and multi-date Cubesat images provided by SkySat and PlanetScope, respectively. We use SkySat data over the Morenci Mine, Arizona, which is the largest copper mine in the United States. The re-projection error after the RFM refinement is 0.42 pix without using ground control points (GCPs). Comparison of our Digital Elevation Model (DEM with ~3 m GSD) with a reference DEM obtained from an airborne LiDAR survey (with ~1 m GSD) over stable areas yields a standard deviation of the elevation differences of ~3.9 m. The comparison of the two DEMs allows detecting and measuring the topographic changes due to the mine activity (excavation and stockpiles). We assess the potential of PlanetScope data, using multi-date DOVE-C images from the Shisper glacier, located in the Karakoram (Pakistan), which is known for its recent surge. We extracted DEMs in 2017 and 2019 before and after the surge. The re-projection error after the RFM refinement is 0.38 pix without using GCPs. The accuracy of our DEMs (with ~9 m GSD) is evaluated through comparison with the SRTM DEM (GSD ~30 m) and with a DEM (GSD ~2 m) calculated from Geoeye-1 (GE-1) and World-View-2 (WV-2) stereo images. The standard deviation of the elevation differences in stable areas between the PlanetScope DEM and SRTM is ~12 m, and ~7 m with the GE-1&WV-2 DEM. The mass transfer due to the surge is clearly revealed from a comparison of the 2017 and 2019 DEMs. The study demonstrates that, with the proposed scheme for RFM optimization, times series of DEM extracted from SkySat and PlanetScope images can be used to measure topographic changes due to mining activities or ice flow, and could also be used to monitor geomorphic processes such as landslides, or coastal erosion for example