The deformation of the Earth surface or of man-made infrastructures + can be studied using satellite Synthetic Aperture Radar (SAR) + Interferometry (InSAR). Thanks to new satellite missions and + improvements in the complex data processing chains, large amounts of + high-quality InSAR data are now readily available. However, some + characteristics of these datasets make them unsuitable to be studied + using conventional (geo)imagery softwares. We present InsarViz, a new + Python tool designed specifically to interactively visualize and + analyze large InSAR datasets.
+Satellite Synthetic Aperture Radar (SAR) Interferometry (InSAR) is + a well-established technique in Earth Observation (EO) that enables + very high precision monitoring of ground displacements (mm/year). This + method combines high spatial resolution data (up to a few meters) and + large coverage capabilities (up to continental scale) with a fairly + high temporal resolution (a few days to a few weeks). It is used to + study a wide range of phenomena that impact the Earth surface + (e.g. earthquakes, landslides, permafrost evolution, volcanoes, + glaciers dynamics, subsidence, building and infrastructure + deformation, etc.).
+For several reasons (data availability, non-intuitive radar image
+ geometry, complexity of the processing, etc.), InSAR has long remained
+ a niche technology and few free open-source tools have been dedicated
+ to it compared to the widely-used multi-purpose optical imagery. Most
+ existing tools are focused on data processing (e.g. ROI_PAC
+ (
However, recent spatial missions like the Sentinel-1 mission of the + European program COPERNICUS, with a systematic background acquisition + strategy and an open data policy, provide unprecedented access to + massive SAR datasets. From these new datasets, a network of thousands + of interferograms can be generated over a single area. The consecutive + step is a time-series analysis which produces a spatiotemporal data + cube: a layer of this data cube is a 2D map that contains the + displacement of each pixel of an image relative to the same pixel in + the reference date image. A typical data cube size is 4000x6000x200, + where 4000x6000 are the spatial dimensions (pixels) and 200 is a + typical number of images taken since the beginning of the spatial + mission.
+The aforementioned tools are not suited to allow fluid and + interactive data visualization of such large and multifaceted + datasets. If data cube visualization is a more generic problem and an + active research topic in EO and beyond, some specifics of InSAR (radar + geometry, wrapped phase, relative measurement in space and in time, + multiple types of products needed for interpretation…) call for a new, + dedicated visualization tool.
+InsarViz was prototyped and designed, and is continuously + developed, in close interaction with the geophysicists (end-users) + through interviews and work observations by the developing team + (UX-design). Our focus is on making this tool ergonomic and intuitive, + and providing pertinent functionalities to explore the datasets, while + maintaining performance and accuracy (stay true to data).
+InsarViz allows visualization and access to data from the + spatiotemporal data cube of InSAR time-series (displacement maps). + When loading such a data cube, the user can visualize and navigate + spatially (general view and synchronized zoomed-in view of a map from + the series) and/or temporally (switch between maps), in radar or + ground geometry. Hovering the cursor on the map directly gives access + to the data from the map and from the whole temporal series (temporal + profile drawn on-the-fly). A separate panel can be used to plot and + extract data from selected points or profiles on the map. A + parametrized trend can then be fitted and subtracted from the observed + data to discern physical processes. Publication-ready figures of the + maps and plots can easily be exported in multiple common formats.
+In future versions of this tool, the user will be able to + concurrently load other images (other products of the processing + chain, DEM, etc.) for further analysis (quality assessment, etc.).
+The main technical characteristics of the tool are:
+-
+
InsarViz is a standalone application that takes advantage of + the hardware (i.e. GPU, SSD hard drive, capability to run on + cluster). We choose the Python language for its well-known + advantages (interpreted, readable language, large community) and + we use QT for the graphical user interface and OpenGL for the + hardware graphical acceleration.
+InsarViz uses the GDAL library
+ (
We take advantage of the Python/QT/OpenGL stack to ensure + efficient user interaction with the data. For example, they allow + the fluid, rapid switching between large maps and on-the-fly + plotting.
+Visualization tools commonly use aggregation methods + (e.g. smoothing, averaging, clustering) to drastically accelerate + image display, but they thus induce observation and interpretation + biases that are detrimental to the user. To avoid those bias, we + focus on staying true to the original data and allowing the user + to customize the rendering manually (color-scale, outliers + selection, level-of-detail).
+The following figure shows a screenshot of the
+
Color on the map shows the displacement with respect to the + previous date (yellow means going away from the satellite). The + colorbar in the middle allows the user to interactively change the + dynamic of the color on the map. The curve on the right shows the + displacement, in the direction of the satellite, of the point under + the mouse (cross). The curve is dynamically updated while the user + moves the mouse on the map.
+Visualisation of a data-cube of Mexico. Displacement at + the localisation of the Puebla Earthquake, 2017/09/19
InsarViz is developed on the Université de Grenoble’s GitLab as an + open-source package, and the authors welcome feature suggestions and + contributions. We use the pytest package to test and ensure the code + quality.
+This project was financially supported by CNES as an application of + the SENTINEL1 mission. The authors would like to thank the Editor and + the Reviewers for their time and comments that helped improve the + manuscript and the code.
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