A method for selective boundary detection includes identifying a plurality of boundaries for a plurality of subregions in a region of interest utilizing one or more multispectral images for the region of interest. The method further includes analyzing a plurality of adjacent fields to a first field in a first subregion out of the plurality of subregions utilizing a region identification criterion based on a plurality of attributes for the first field and the plurality of adjacent fields. The method further includes determining, based on the analyzing, the first region with the first field requires further analysis of multitemporal remote sensed data over a defined period of time.

Described is a method for extraction of a region of interest (ROI) from a composite synthetic aperture radar (SAR) phase history data. The method comprising receiving, with a system comprising a processor, the composite SAR phase history data of a plurality of backscattered return signals produced by a SAR system illuminating a scene with a SAR beam. The method also comprises obtaining a location of a first ROI within the scene and extracting from the composite SAR phase history data a first component SAR phase history data corresponding to the ROI at the location of the ROI.

Disclosed are systems and techniques for detecting user presence, user motion, and for performing facial authentication. For instance, a wireless device can receive a waveform that is a reflection of a transmitted radio frequency (RF) waveform. Based on RF sensing data associated with the received waveform, the wireless device can determine a presence of a user. In response to determining the presence of the user, the wireless device can initiate facial authentication of the user.

For example, a processor may be configured to process point cloud (PC) radar information comprising radar detection information of a plurality of possible detections, wherein radar detection information corresponding to a possible detection of the plurality of possible detections comprises information of a plurality of radar attributes of the possible detection, wherein the processor is configured to determine a plurality of validity scores corresponding to the plurality of possible detections based on the radar detection information of the plurality of possible detections, a validity score corresponding to the possible radar detection to indicate whether it is more probable that the possible detection is a valid detection or a False-Alarm (FA) detection, wherein the processor is to output radar target information based on the plurality of validity scores corresponding to the plurality of possible detections.

A method of identifying a target from synthetic aperture radar (SAR) data without incurring the computational load associated with generating an SAR image. The method includes receiving SAR data collected by a radar system including RF phase history data associated with reflected RF pulses from a target in a scene, but excluding an SAR image. Range profile data is determined from the SAR data by converting the RF phase history data into a structured temporal array that can be applied as input to a classifier incorporating a recurrent neural network, such as a recurrent neural network made up of long short-term memory (LSTM) cells that are configured to recognize temporal or spatial characteristics associated with a target, and provide an identification of a target based on the recognized temporal or spatial characteristic.

A cloud platform-based garlic crop recognition method by coupling active and passive remote sensing images includes: firstly, obtaining an optical satellite remote sensing image based on phenological characteristics of garlic, and constructing a decision tree model for optical image recognition of the garlic by combining geographic coordinate information of the garlic, so as to obtain an optical distribution diagram of the garlic; secondly, obtaining radar image characteristics of the garlic and winter wheat based on a synthetic aperture radar satellite, and constructing a decision tree model for radar image recognition of the garlic by combining the geographic coordinate information of the garlic, so as to obtain a radar distribution diagram of the garlic; and finally, coupling the optical distribution diagram of the garlic with the radar distribution diagram of the garlic, i.e., selecting an intersection of the two distribution diagrams to complete remote sensing recognition drawing of the garlic.

A method of measuring land deformation over time using interferograms derived from synthetic aperture radar data. The method includes: acquiring radar images covering an area at different points in time; deriving interferograms from pairs of the images, each interferogram measuring phase difference between pixels of a respective pair of images; for each pixel of the interferograms: determining an average coherence value over all of the interferograms; and if the average value is less than a threshold, determining an adjusted average coherence value equal to or above the threshold by excluding one or more of the interferograms below the threshold, provided the number of remaining interferograms is not less than a preset minimum number for each pixel of each interferogram for which the average or adjusted average coherence value is above the threshold, deriving vertical movement from the phase difference; and deriving the map of land deformation from the vertical movement.

A system and a method for determining infrastructure risk zones is disclosed. The system and method may include: receiving, from a radiofrequency (RF) radiation sensor, a first scan of an area, wherein the area at least partially comprises the infrastructure; receiving additional data; filtering electromagnetic noise from the first scan using the additional data; receiving infrastructure location in the area; determining an examination zone around the infrastructure; estimating the amount of clay in soil included in the examination zone, from the filtered scan; calculating soil moisture content at locations in the examination zone, from the filtered scan; and determining location at risk having soil moisture content above a predetermined threshold, wherein the threshold may be determined based on the estimated clay amount.

A learning data generation device includes: a target object image generating unit for simulating radar irradiation to a target object using a 3D model of the target object to generate a target object-simulated radar image that is a simulated radar image of the target object; a background image acquiring unit for acquiring a background image using radar image information generated by the radar device performing radar irradiation; an image combining unit for generating a combined pseudo radar image obtained by combining the background image and the target object-simulated radar image by pasting the target object-simulated radar image generated by the target object image generating unit to a predetermined position in the background image acquired by the background image acquiring unit; and a learning data generating unit for generating learning data that associates combined simulated radar image information indicating the combined pseudo radar image generated by the image combining unit with class information indicating a type of the target object.

A system configured to identify a target in a synthetic aperture radar signal includes: a feature extractor configured to extract a plurality of features from the synthetic aperture radar signal; an input spiking neural network configured to encode the features as a first plurality of spiking signals; a multi-layer recurrent neural network configured to compute a second plurality of spiking signals based on the first plurality of spiking signals; a readout neural layer configured to compute a signal identifier based on the second plurality of spiking signals; and an output configured to output the signal identifier, the signal identifier identifying the target.