A microwave single pixel imager apparatus and method of using same. Sampling a targeted scene includes the following. A plurality of modulated antenna patterns is generated using a reflectarray. A plurality of antenna temperatures respectively corresponding to the plurality of modulated antenna patterns is measured. A retrieved scene corresponding to the sampled targeted scene is generated. Generating a retrieved scene corresponding to the sampled targeted scene includes the following. The plurality of modulated antenna patterns and the corresponding plurality of antenna temperatures are fed into a compressive sensing imaging algorithm.

A system and method for generating soil moisture data from satellite images of a geographical area using a deep learning model 108 is provided. The system includes one or more satellites 102A-C, a soil moisture data generator server 106. The method includes, (i) receiving, by a soil moisture data generator server, satellite images of the geographical area, (ii) pre-processing first set of satellite images, second set of satellite images, and third set of satellite images, (iii) interpolating, using spline interpolation, pre-processed first set of images, pre-processed second set of images, and pre-processed third set of images to generate high-resolution set of images, (iv) generating hydrological parameters from the high-resolution set of images, (v) training, a deep learning model, by providing historical hydrological parameters and historical soil moisture data associated with historical satellite images as training data to generate trained deep learning model, (v) generating soil moisture data on daily basis.

A system, and method of operating the same detects moving targets in images and performs image turbulence correction. The system includes an automatic target recognizer (ATR) system including a database. The ATR includes a feature extractor and processor arranged to detect a plurality of reference features associated with targets within image frames, and calculate a position of the plurality of reference features. The system includes an image processor arranged to receive the position, demosaic the image frames into a plurality of video tiles, iteratively process the video tiles for turbulence correction to generate turbulence corrected video tiles associated with acquired targets; convert the turbulence corrected video tiles into a single video frame tile including turbulence degradation correction; and mosaic each of the single video frame tiles to generate a full field of view turbulence corrected video stream.

Examples disclosed herein are related to using a machine learning model to generate image data. One example provides a system, comprising one or more processors, and storage comprising instructions executable by the one or more processors to obtain image data comprising an image with unoccluded features, apply a mask to the unoccluded features in the image to form partial observation training data comprising a masked region that obscures at least a portion of the unoccluded features, and train a machine learning model comprising a generator and a discriminator at least in part by generating image data for the masked region and comparing the image data generated for the masked region to the image with unoccluded features.

Linear interpolation is performed within a memory system. The memory system receives a floating-point point index into an integer-indexed memory array. The memory system accesses the two values of the two adjacent integer indices, performs the linear interpolation, and provides the resulting interpolated value. In many system architectures, the critical limitation on system performance is the data transfer rate between memory and processing elements. Accordingly, reducing the amount of data transferred improves overall system performance and reduces power consumption.

A distributed microwave radar imaging method includes obtaining a first echo signal received by a first microwave radar, where the first microwave radar is disposed at a first height; obtaining a second echo signal received by a second microwave radar, where the second microwave radar is disposed at a second height, and the first height is lower than the second height; determining a first radar imaging result image of a detected target based on the first echo signal; determining a second radar imaging result image of the detected target based on the second echo signal; fusing the first radar imaging result image and the second radar imaging result image to obtain a target fused image; and determining outline information of the detected target based on the target fused image.

Embodiments are disclosed that for synthetic aperture radar (SAR) systems and methods that process radar image data to generate radar images using vector processor engines, such as single-instruction-multiple-data (SIMD) processor engines. The vector processor engines can be further augmented with accelerators that vectorize element selection thereby expediting memory accesses required for interpolation operations performed by the vector processor engines.

A method (100) for referencing an optical image (19) including: obtaining (110, 120) a stereoscopic image pair (19, 23) of the optical image (19) and a SAR image (35), the surface areas covered by the images (19, 23, 35) on the ground having an overlapping area (39); selecting (130) an area of interest (42) in the overlapping area (39); from the area of interest (42): obtaining (140) a 3D model (40); calculating (150) a simulated radar image (44); estimating (160) an offset (di, dj) between the simulated image (44) and the radar image (35); selecting (170) a reference point (46); projecting (180) and shifting (di, dj) the reference point (46) in the radar image (35) to correct the radar connection point (46′″); determining (175) a pair of connection points (46′, 46″) in the image pair; and referencing the optical image (19) based on the connection points (46′, 46″, 46′″).

A system, and method of operating the same detects moving targets in images and performs image turbulence correction. The system includes an automatic target recognizer (ATR) system including a database. The ATR includes a feature extractor and processor arranged to detect a plurality of reference features associated with targets within image frames, and calculate a position of the plurality of reference features. The system includes an image processor arranged to receive the position, demosaic the image frames into a plurality of video tiles, iteratively process the video tiles for turbulence correction to generate turbulence corrected video tiles associated with acquired targets; convert the turbulence corrected video tiles into a single video frame tile including turbulence degradation correction; and mosaic each of the single video frame tiles to generate a full field of view turbulence corrected video stream.

A unified carrier cargo rack and storage system for a vehicle including a receiver unit installed in the rear of the vehicle and a modular carrier rack, including a transitional carrier bar, a secondary bar and a cargo carrier bar, wherein the cargo carrier bar is further operable to engage and immobilize cargo; are disclosed as are male and female securing mechanisms.