Co-Registration of Optically Sensed Images and Correlation (COSI-Corr): an Operational Methodology for Ground Deformation Measurements

Recent methodological progress, Co-Registration of Optically Sensed Images and Correlation, outlined here, makes it possible to measure horizontal ground deformation from optical images on an operational basis, using the COSI-Corr software package. In particular, its sub-pixel capabilities allow for accurate mapping of surface ruptures and measurement of co-seismic offsets. We retrieved the fault rupture of the 2005 Mw 7.6 Kashmir earthquake from ASTER images, and we also present a dense mapping of the 1992 Mw 7.3 Landers earthquake of California, from the mosaicking of 30 pairs of aerial images

Accuracy analysis of direct georeferenced UAV images utilising low-cost navigation sensors

Unmanned aerial vehicles (UAVs), also known as unmanned airborne systems (UAS) or remotely piloted airborne systems (RPAS), are an established platform for close range airborne photogrammetry. Compared to manned platforms, the acquisition of local remote sensing data by UAVs is a convenient and very flexible option. For the application in photogrammetry UAVs are typically equipped with an autopilot and a lightweight digital camera. The autopilot includes several navigation sensors, which might allow an automated waypoint flight and offer a systematic data acquisition of the object resp. scene of interest. Assuming a sufficient overlap between the captured images, the position (3 coordinates: x, y, z) and the orientation (3 angles: roll, pitch, yaw) of the images can be estimated within a bundle block adjustment. Subsequently, coordinates of observed points that appear in at least two images, can be determined by measuring their image coordinates or a dense surface model can be generated from all acquired images by automated image matching. For the bundle block adjustment approximate values of the position and the orientation of the images are needed. To gather this information, several methods exist. We introduce in this contribution one of them: the direct georeferencing of images by using the navigation sensors (mainly GNSS and INS) of a low-cost on-board autopilot. Beside automated flights, the autopilot offers the possibility to record the position and the orientation of the platform during the flight. These values don’t correspond directly to those of the images. To compute the position and the orientation of the images two requirements must be fulfilled. First the misalignment angles and the positional differences between the camera and the autopilot must be determined (mounting calibration). Second the synchronization between the camera and the autopilot has to be established. Due to the limited accuracy of the navigation sensors, a small number of ground control points should be used to improve the estimated values, especially to decrease the amount of systematic errors. For the bundle block adjustment the calibration of the camera and their temporal stability must be determined additionally. This contribution presents next to the theory a practical study on the accuracy analysis of direct georeferenced UAV imagery by low-cost navigation sensors. The analysis was carried out within the research project ARAP (automated (ortho)rectification of archaeological aerial photographs). The utilized UAS consists of the airplane “MAJA”, manufactured by “Bormatec” (length: 1.2 m, wingspan: 2.2 m) equipped with the autopilot “ArduPilot Mega 2.5”. For image acquisition the camera “Ricoh GR Digital IV” is utilised. The autopilot includes a GNSS receiver capable of DGPS (EGNOS), an inertial measurement system (INS), a barometer, and a magnetometer. In the study the achieved accuracies for the estimated position and orientation of the images are presented. The paper concludes with a summary of the remaining error sources and their possible corrections by applying further improvements on the utilised equipment and the direct georeferencing process

An insight of the VHR European data: The processing of the new VHR_IMAGE_2015 using the GHSL tools. A feasibility production test.

The European Union and the European Space Agency funded the CSC-Data Access project in the frame of the European Community's Seventh Framework Programme FP7/2007-2013 and the Multiannual Financial Framework Programme (MFF). It is an integral part of the GMES/Copernicus Space Component Programme. The CORE datasets aim at consolidating predefined needs collected from Copernicus services and other activities requesting Earth Observation data whether financed by the Union or related to Union’s policies, like Union financed research projects and the activities of Union agencies (EEA, EMSA, SatCen, etc.) For an initial period of 2007-2011, data access management was funded through a data access grant between the EC and ESA. In 2012 the JRC, taking advantage of the first wall-to-wall European dataset with very high resolution (DWH_MG2b_CORE_03) and being financed by DG REGIO, derived the first European built-up layer, with related information using the GHSL tool, ad hoc tuned on the input dataset (EU GHSL). Through the Data Access Portfolio (DAP) and using the GHSL tool, the JRC Disaster Risk Management Unit could provide European wall-to-wall maps of settlements evolution until 2020. According to the Data Warehouse document which describes the data availability, the acquisition campaign shall be repeated every 3 years taking 2012 as starting reference. Under this condition, at least 2 European mosaic shall be produced (2015 and 2018 referenced). Considering also the DWH_MG2b_CORE_03, which has been already processed and delivered, maps of settlements of three epochs (2012, 2015, 2018) would be available for studying urban and rural phenomena and trends. The report illustrates the results of initial tests conducted using the EU GHSL tool on the new VHR_IMAGE_2015, which promises to be much more accurate and better documented than the previous one.JRC.E.1-Disaster Risk Managemen

Technical aspects of Envisat-ASAR geocoding capability at DLR

Based on experience with the geocoding systems for ERS-D-PAF (GEOS), the SIR-C/X-SAR (GEOS) and SRTM missions (GeMoS), geocoding functionality has been extended for Envisat ASAR data. The existing Envisat ASAR Geocoding System (EGEO) can handle all Level 1-b image products (IMS, APS, IMP, APP, IMM, APM, WSM and GM1). Complementary to geocoded products provided by ESA (IMG, APG) the geocoding procedure applied at the German Aerospace Center (DLR) makes use of a DEM to achieve higher geolocation accuracy. The resulting geocoded image is either defined as EEC (Enhanced Ellipsoid Corrected) or as ETC (Enhanced Terrain Corrected). These products mainly differ in the underlying DEM used for geocoding. The EEC utilizes GLOBE, while the ETC utilizes the “best” DEM available in the data base. This “best” DEM can be assembled from different DEM data sets (e.g. derived from SRTM, ERS, …). Further differences such as the interpolative (EEC) and rigorous (ETC) geocoding approach will also be outlined. Furthermore, an incidence angle mask can be generated. The necessary upgrades for geocoding ASAR stripline products (e.g. IMM, WSM) will be presented. Stripline products cover a large area along track, as they consist of concatenated stand-alone products (“slices”). Thus the updates of relevant parameters have to be taken into account

Influence of camera distortions on satellite image registration and change detection applications

Applications such as change detection and digital elevation model extraction from optical images require a rigorous modeling of the acquisition geometry. We show that the unrecorded satellite jitter during image acquisition, and the uncertainties on the CCD arrays geometry are the current major limiting factors for applications requiring high accuracy. These artifacts are identified and quantified on several optical satellites, i.e., SPOT, ASTER, QuickBird, and HiRISE

Investigation on the automatic geo-referencing of archaeological UAV photographs by correlation with pre-existing ortho-photos

We present a method for the automatic geo-referencing of archaeological photographs captured aboard unmanned aerial vehicles (UAVs), termed UPs. We do so by help of pre-existing ortho-photo maps (OPMs) and digital surface models (DSMs). Typically, these pre-existing data sets are based on data that were captured at a widely different point in time. This renders the detection (and hence the matching) of homologous feature points in the UPs and OPMs infeasible mainly due to temporal variations of vegetation and illumination. Facing this difficulty, we opt for the normalized cross correlation coefficient of perspectively transformed image patches as the measure of image similarity. Applying a threshold to this measure, we detect candidates for homologous image points, resulting in a distinctive, but computationally intensive method. In order to lower computation times, we reduce the dimensionality and extents of the search space by making use of a priori knowledge of the data sets. By assigning terrain heights interpolated in the DSM to the image points found in the OPM, we generate control points. We introduce respective observations into a bundle block, from which gross errors i.e. false matches are eliminated during its robust adjustment. A test of our approach on a UAV image data set demonstrates its potential and raises hope to successfully process large image archives

Working towards an Improved Monitoring Infrastructure to support Disaster Management, Humanitarian Relief and Civil Security

Within this paper experiences and results from the work in the context of the European Initiative on Global Monitoring for Environment and Security (GMES) as they were gathered within the German Remote Sensing Data Center (DFD) are reported. It is described how data flows, analysis methods and information networks can be improved to allow better and faster access to remote sensing data and information in order to support the management of crisis situations. This refers to all phases of a crisis or disaster situation, including preparedness, response and recovery. Above the infrastructure and information flow elements, example cases of different crisis situations in the context of natural disasters, humanitarian relief activities and civil security are discussed. This builds on the experiences gained during the very active participation in the network of Excellence on Global Monitoring for Stability and Security (GMOSS), the GMES Service Element RESPOND, focussing on Humanitarian Relief Support and supporting the International Charter on Space and Major Disasters as well as while linking closely to national, European and international entities related to civil human security. It is suggested to further improve the network of national and regional centres of excellence in this context in order to improve local, regional and global monitoring capacities. Only when optimum interoperability and information flow can be achieved among systems and data providers on one hand side and the decision makers on the other, efficient monitoring and analysis capacities can be established successfully

High resolution thermal and multispectral UAV imagery for precision assessment of apple tree response to water stress

UMR AGAP - équipe AFEF - Architecture et fonctionnement des espèces fruitières(Edited by Pablo Gonzalez-de-Santos and Angela Ribeiro)This manuscript presents a comprehensive methodology to obtain Thermal, Visible and Near Infrared ortho-mosaics, as a previous step for the further image-based assessment of response to water stress of an experimental apple tree orchard. Using this methodology, multi-temporal ortho-mosaics of the field plot were created and accuracy of ortho-rectification and geo-location computed. Unmanned aerial vehicle (UAV) flights were performed on an irrigated apple tree orchard located in Southern France. The 6400 m² plot was composed of 520 apple trees which were disposed in 10 rows. In this field set-up, five well irrigated rows alternated with five rows submitted to progressive summer water constraints. For remote image acquisition, on 4th July, 19th July, 1st August and 6th September UAV flights with three cameras onboard (thermal, visible and near infrared) were performed at solar noon. On 1st August, five successive UAV flights were carried out at 8, 10, 12, 14 and 16 h (solar time). By using selfdeveloped software, frames were automatically extracted from the recorded thermal video and turned in the right image format. The temperature of four different targets (hot, cold, wet and dry bare soil) was continuously measured by the IR120 thermoradiometers during each flight, for radiometric calibration purpose. Based each on thirty images, all ortho-mosaics were successfully obtained. As high spatial resolution imagery requires high precision geo-location, and the root mean squared error (RMSE) of each ortho-mosaic positioning was calculated in order to assess its spatial accuracy. RMSE values were less than twice the pixel size in every case, which allowed a precise overlapping of the mosaics created. Canopy temperature data extracted from thermal images for showed significantly higher temperatures in water stressed trees compared to well irrigated, difference being related to severity of water stress. Thanks to the ultrahigh resolution of remote images obtained (<0.1m spatial resolution for thermal infrared images), and beyond its capacity to delineate efficiently each individual tree, the methodology presented here will also make it possible the analysis of intra-canopy variations and the accurate calculation of vegetation and water stress indices

GHSL/UA Integration: Feasibility Report. Application of the JRC GHSL Image Information Extraction Protocol in the frame of the Urban Atlas product specifications

JRC started the design of the global human settlement layer (GHSL) concept during 2010-2011, together with the development of an image query (IQ) system able to generate and manage geoinformation in an integrated way. The IQ system aggregated the experiences related to automatic information extraction from meter and sub-metre resolution satellite image data in the disaster and crisis management scenarios supported by JRC since 2003-2004. The first alpha-test of the IQ system was delivered in Dec 2011, performing a GHSL image information query task over high and very-high resolution satellite image data covering more than 615 billions of square kilometres of global earth surface, mostly placed in populated regions of Europe, Africa, Asia and South America. During 2011, first contacts with DGREGIO were made in order to understand if the JRC IQ technology and the derived GHSL information layers may be of interest in the context of the “European Urban Atlas” (UA) implementation and in general, in pan-European mapping and characterization of European settlements. This feasibility report describes the application of the GHSL protocol according to the Urban Atlas product specifications and more specifically the comparison between SSL output information with the GHSL built-up information extraction in the context of the Urban Atlas 2012-2013. The objectives of the work described in this report were i) to test the processing capacity of the JRC IQ system in order to assess the feasibility of a pan-European GHSL coverage or “built-upareas detection” using the image data prepared for the UA 2012-2013, ii) to assess the reliability and added value of the automatic image information retrieval by systematic comparison of the automatic output with a known reference layer reporting about similar information, namely, the European soil sealing layer.JRC.G.2-Global security and crisis managemen