SYNTHETIC APERTURE RADAR DATASETS

Advanced applications such as Automatic Target Recognition or Artificial Intelligence require the availability of large datasets which can be used as target radar signatures or labelled target databases, respectively.The datasets, to be effective in the further algorithm developments, must be created with the full knowledge of the input, configurations, environmental conditions and output. The only way to have well-defined datasets is by using realistic simulations which provide synthetic datasets with well-known properties, i.e. labelled targets with their locations and orientations, radar viewing geometry, multiple acquisitions modes. Moreover, the creation of Synthetic Aperture Radar image datasets with real acquired flying SAR sensors is costly, tedious and time consuming for the data polishing and correction and the labelling of the targets in the data. The solution to the creation of large SAR datasets with data augmentation is the use of the advanced realistic SAR simulator developed by MetaSensing. You can create your own SAR dataset.

Example of SAR simulations for five artillery trucks in a urban background. Upper row from left to right: CAD model of the truck, picture of the created scene with five trucks in a urban road, 3D RCS point cloud output of the EM-solver.

Lower row from left to right: graphical optical view of the scene from above, synthetic SAR range compressed data, synthetic SAR image with labelled targets, zoom in on the targets labelled for DeepLearning and MachineLearning applications.

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Key features:• 100+ targets divided in 5 classes (Naval, Air, Land, Missile, Radar) and 14 subclasses• 1200+ background divided in 6 classes (Urban, Airport, Forest, Harbor, Mountain, Sea)• Full simulation chain: Scene (background + targets) generation, 3D RCS point cloud generator (EM-Solver), SAR raw data generator, SAR image creation• Labelled targets at every stage of the simulation output• Output in standard formats (.HDF, .GeoJSON, .GeoTIFF)• Simulation of Airborne and Spaceborne SAR satellites• Flexible configurations for different SAR sensors and trajectory (frequency, polarization, antenna, PRF, bandwidth, resolution, power, gains, trajectory, IMU, etc.) • Multiple SAR operational modes: Stripmap, Spotlight, MTI, STAP, High-Squint• Multi-frequency simulations (P, L, S, C, X, Ku, Ka, W) • Polarimetric simulations (HH, HV, VH, VV)• Fast and precise simulations with state-of-the-art GPU technology (hardware accelerated ray-tracing) (i.e. single target (tank) at 15cm resolution, total simulation time: 16 seconds; 1x1 km2 scene with 100 targets (tanks) at 15cm resolution, total simulation time: 17 minutes) • Parallel processing with multiple GPU cards (i.e. 10,000 scenes of 1x1 km2 with 100 tanks each at 15 cm resolution, total simulation time: ~ 12 days)

Multiple datasets options:• Custom datasets/databases creation based on user requirements • Dataset for Automatic Target Recognition: data with multiple SAR viewing geometries, polarimetry and different target orientations• Dataset for Deep Learning & Machine Learning: database with 10000+ scenes with labelled targets• Dataset with Polarimetry: dataset with multiple targets and polarimetric SAR images at different frequencies• Dataset for Multifrequency analysis: dataset with SAR images of the same scene simulated with multiple frequencies

Example of SAR simulations for jet fighter aircraft. Top-left: view of the 3D CAD of the target; Bottom-left: 3D RCS point cloud output of the EM-solver; Right-half: Polarimetric simulated SAR images for the jet fighter aircraft.