Systems and methods for satellite Synthetic Aperture Radar (SAR) artifact suppression for enhanced three-dimensional feature extraction, change detection, and/or visualizations are described. In some aspects, the described systems and methods include a method for suppressing artifacts from complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for creating a photo-realistic 3D model of a scene based on complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for identifying three-dimensional (3D) features and changes in SAR imagery.

A computer includes a processor and a memory storing instructions executable by the processor to receive radar data including a radar pixel having a radial velocity from a radar; receive camera data including an image frame including camera pixels from a camera; map the radar pixel to the image frame; generate a region of the image frame surrounding the radar pixel; determine association scores for the respective camera pixels in the region; select a first camera pixel of the camera pixels from the region, the first camera pixel having a greatest association score of the association scores; and calculate a full velocity of the radar pixel using the radial velocity of the radar pixel and a first optical flow at the first camera pixel. The association scores indicate a likelihood that the respective camera pixels correspond to a same point in an environment as the radar pixel.

The invention concerns a method (1) to detect deformations of, and/or damages to, structures permanently arranged on the earth's surface. In particular, said method (1) comprises: acquiring (11) georeferencing data indicative of geographical reference positions of predefined points of interest of a given structure to be monitored permanently arranged on the earth's surface, wherein said predefined points of interest are representative of a 3D geometry of the given structure without deformations and damages; acquiring (12) a SAR image of an area of the earth's surface where the given structure is arranged, wherein said SAR image is associated with a given reference coordinate system; transforming (13) the geographical reference positions of the predefined points of interest into corresponding expected positions in the given reference coordinate system associated with the acquired SAR image so as to carry out a reprojection of the 3D geometry of the given structure without deformations and damages on the acquired SAR image; identifying (14) in the acquired SAR image the predefined points of interest of the given structure; determining (15) actual positions in the given reference coordinate system associated with the acquired SAR image of the predefined points of interest identified in said SAR image; making a comparison (16) between the expected positions of the predefined points of interest and the corresponding actual positions in the acquired SAR image; and detecting (17) one or more deformations of, and/or one or more damages to, said given structure on the basis of the comparison made.

The present disclosure discloses a microwave identification method, which is implemented on at least one device, including at least one processor and at least one storage device, the method including: the at least one processor obtains microwave data; the at least one processor generates an image of one or more objects based on the microwave data; the at least one processor obtains a model of each of the one or more objects; and based on the model of each of the one or more objects, the at least one processor identifies the one or more objects in the image of the one or more objects.

This document describes techniques for enabling de-aliased imaging for a synthetic aperture radar. Radar signals processed by a synthetic aperture radar (SAR) system may include false detections in the form of aliasing induced by grating lobes. The techniques described herein can reduce the adverse effects of grating lobes by obtaining an initial SAR image using a back-projection algorithm. Aliasing effects (e.g., false detections) in this initial image may be common due to the limitations of an SAR system moving at non-uniform speeds. A refined image is produced from the initial image by applying a de-aliasing filter to the initial image. The refined image may have reduced or eliminated false detections that attribute to aliasing effects, resulting in a better representation of the environment of the vehicle.

Radar sensor for motor vehicles. The radar sensor has a high-frequency part, which is configured to transmit sequences of modulated radar pulses and to receive the corresponding radar echoes, and an electronic evaluation part, which is configured to take distance and angle measurements using a synthetic aperture, and includes a scanning module, an FFT module for performing fast Fourier transforms to calculating a two-dimensional distance/velocity radar image, a transform module configured to transform the raw data, while simultaneously correcting migration effects, into a format that can be processed by the FFT module, by applying a transform function defined by a number N of coefficients, and a coefficient module for preparing the coefficients for the transform module. The coefficient module including a memory, in which there is stored an initial set of coefficients comprising fewer than N coefficients, and a recursion module, for recursive calculation of the remaining coefficients.

A radar image processing device performs determination of a pixel including a ghost image and changes the value of the pixel which is determined to include the ghost image on a radar image the focus of which has been changed.

A method for training a trainable module for evaluating radar signals. The method includes feeding actual radar signals and/or actual representations derived therefrom of a scene observed using the actual radar signals to the trainable module and conversion thereof by this trainable module to processed radar signals and/or to processed representations of the respective scene, and using a cost function to assess to what extent the processed radar signals are suited for reconstructing a movement of objects or to what extent the processed representations contain artifacts of moving objects in the scene. Parameters, which characterize the performance characteristics of a trainable module, are optimized with regard to the cost function. A method is also provided for evaluating moving objects from radar signals.

An image processing apparatus according to the present invention includes: an extracting unit configured to extract a candidate image, which is an image of a candidate region specified in accordance with a preset criterion, from a target image to be a target for an annotation process, and also extract a corresponding image, which is an image of a corresponding region corresponding to the candidate region, from a reference image that is an image corresponding to the target image; a displaying unit configured to display the candidate image and the corresponding image so as to be able to compare the images with each other; and an input accepting unit configured to accept input of input information for the annotation process for the candidate image.

The present invention relates to a method and apparatus that can predict the visible-infrared band images of a region of the Earth's surface that would be observed by an Earth Observation (EO) satellite or other high-altitude imaging platform, using data from radar reflectance/backscatter of the same region. The method and apparatus can be used to predict images of the Earth's surface in the visible-infrared bands when the view between an imaging instrument and the ground is obscured by cloud or some other medium that is opaque to electromagnetic (EM) radiation in the visible-infrared spectral range, approximately spanning 400-2300 nanometres (nm), but transparent to EM radiation in the radio-/microwave part of the spectrum. Regular, uninterrupted monitoring of the Earth's surface is important for a wide range of applications, from agriculture to defence.