An Efficient Vehicle Localization Method by Using Monocular Vision

Accurate localization of surrounding vehicles helps drivers to perceive surrounding environment, which can be obtained by two parameters: depth and direction angle. This research aims to present a new efficient monocular vision based pipeline to get the vehicle’s location. We proposed a plug-and-play convolutional block combination with a basic target detection algorithm to improve the accuracy of vehicle’s bounding boxes. Then they were transformed to actual depth and angle through a conversion method which was deduced by monocular imaging geometry and camera parameters. Experimental results on KITTI dataset showed the high accuracy and efficiency of the proposed method. The mAP increased by about 2% with an additional inference time of less than 5 ms. The average depth error was about 4% for near distance objects and about 7% for far distance objects. The average angle error was about two degrees.

SAR Image Simulation of Complex Target Including Multiple Scattering

We present a GPU-based computation for simulating the synthetic aperture radar (SAR) image of the complex target. To be more realistic, we included the multiple scattering field and antenna pattern tracking in producing the SAR echo signal for both Stripmap and Spotlight modes. Of the signal chains, the computation of the backscattering field is the most computationally intensive. To resolve the issue, we implement a computation parallelization for SAR echo signal generation. By profiling, the overall processing was identified to find which is the heavy loading stage. To further accommodate the hardware structure, we made extensive modifications in the CUDA kernel function. As a result, the computation efficiency is much improved, with over 224 times the speed up. The computation complexity by comparing the CPU and GPU computations was provided. We validated the proposed simulation algorithm using canonical targets, including a perfectly electric conductor (PEC), dielectric spheres, and rotated/unrotated dihedral corner reflectors. Additionally, the targets can be a multi-layered dielectric coating or a layered medium. The latter case aimed to evaluate the polarimetric response quantitively. Then, we simulated a complex target with various poses relative to the SAR imaging geometry. We show that the simulated images have high fidelity in geometric and radiometric specifications. The decomposition of images from individual scattering bounce offers valuable exploitation of the scattering mechanisms responsible for imaging certain target features.

Onboard Digital Beamformer with Multi-Frequency and Multi-Group Time Delays for High-Resolution Wide-Swath SAR

Scan-on-receive (SCORE) digital beamforming (DBF) in elevation can significantly improve the signal-to-noise ratio (SNR) and suppress range ambiguities in spaceborne synthetic aperture radar (SAR). It has been identified as one of the important methods to obtain high-resolution wide-swath (HRWS) SAR images. However, with the improvement of geometric resolution and swath width, the residual pulse extension loss (PEL) due to the long pulse duration in the conventional spaceborne onboard DBF processor must be considered and reduced. In this paper, according to the imaging geometry of the spaceborne DBF SAR system, the reason for the large attenuation of the receiving gain at the edge of the wide swath is analyzed, and two improved onboard DBF methods to mitigate the receive gain loss are given and analyzed. Taking account of both the advantages and drawbacks of the two improved DBF methods presented, a novel onboard DBF processor with multi-frequency and multi-group time delays in HRWS SAR is proposed. Compared with the DBF processor only with multi-group time delays, the downlink data rate was clearly reduced, while focusing performance degradation due to phase and amplitude errors between different frequency bands could be mitigated compared with the DBF processor only with multi-frequency time delays. The simulation results of both point and distributed targets validate the proposed DBF processor.

Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN

High-quality three-dimensional (3-D) radar imaging is one of the challenging problems in radar imaging enhancement. The existing sparsity regularizations are limited to the heavy computational burden and time-consuming iteration operation. Compared with the conventional sparsity regularizations, the super-resolution (SR) imaging methods based on convolution neural network (CNN) can promote imaging time and achieve more accuracy. However, they are confined to 2-D space and model training under small dataset is not competently considered. To solve these problem, a fast and high-quality 3-D terahertz radar imaging method based on lightweight super-resolution CNN (SR-CNN) is proposed in this paper. First, an original 3-D radar echo model is presented and the expected SR model is derived by the given imaging geometry. Second, the SR imaging method based on lightweight SR-CNN is proposed to improve the image quality and speed up the imaging time. Furthermore, the resolution characteristics among spectrum estimation, sparsity regularization and SR-CNN are analyzed by the point spread function (PSF). Finally, electromagnetic computation simulations are carried out to validate the effectiveness of the proposed method in terms of image quality. The robustness against noise and the stability under small are demonstrate by ablation experiments.

Characterization and Pilot Human Trial of Dedicated Breast Ring Positron Emission Tomography (BRPET) System

We describe the design and performance of BRPET, a novel dedicated breast PET (dbPET) scanner designed to maximize visualization of posterior regions of the breast. BRPET uses prone imaging geometry and a 12-module detector ring built from pixelated LYSO crystals coupled to position sensitive photomultiplier tubes (PSPMTs). Optical coupling via slanted plastic fiber optic light guides permits partial insertion of the crystals into the exam table’s breast aperture. Image quality testing procedures were adapted from the NEMA NU4-2008 protocol. Two additional phantom tests quantified the posterior extent of the usable volume of view (VoV). BRPET axial, radial, and tangential FWHM spatial resolutions at the isocenter were 1.8, 1.7, and 1.9 mm, respectively. The peak absolute system sensitivity was 0.97% using an energy window of 460–562 keV. The peak noise equivalent counting rate was 5.33 kcps at 21.6 MBq. The scanner VoV extends to within ~6 mm of the plane defining the location of the chest wall. A pilot human study (n = 10) compared the diagnostic performance of FDG-BRPET to that of contrast enhanced MRI (CEMRI), with biopsy as ground truth. Averaged over three expert human observers, the sensitivity/specificity for BRPET was 0.93/1.0, compared to 1.0/0.25 for CEMRI.

Capability of Airline Jets as an Observation Platform for Noctilucent Clouds at Middle Latitudes

The exact occurrence frequency of noctilucent clouds (NLCs) in middle latitudes is significant information because it is thought to be sensitive to long-term atmospheric change. We conducted NLC observation from airline jets in the Northern Hemisphere during the summer 2019 to evaluate the effectiveness of NLC observation from airborne platforms. By cooperating with the Japanese airline All Nippon Airways (ANA), imaging observations of NLCs were conducted on 13 flights from Jun 8 to Jul 12. As a result of careful analysis, 8 of these 13 flights were found to successfully detect NLCs from middle latitudes (lower than 55°N) during their cruising phase. Based on the results of these test observations, it is shown that an airline jet is a powerful tool to continuously monitor the occurrence frequency of NLCs at midlatitudes which is generally difficult with a polar orbiting satellite due to sparse sampling in both temporal and spatial domain. The advantages and merits of NLC observation from jets over satellite observation from a point of view of imaging geometry is also presented.

Spinor-Based Attitude Determination with Star Sensor Considering Depth

A star sensor is a high-precision satellite attitude measurement device. Since its observation information has only two-dimensional direction vectors, when a star sensor is used for attitude determination the dimension of the observation information is less than the number of attitude angles determined, so mainstream algorithms usually only guarantee the accuracy of the pitch angle and the roll angle. In view of the lack of depth information in the observation's imaging geometric condition, this article proposes a spinor-based attitude determination model, which describes a straight line passing through two stars with the spinor and maps the depth information of the straight line with the pitch, to establish an imaging geometry model of the spinor coplanar condition. Experiments show that the yaw-angle attitude accuracy of the method is an order of magnitude better than that of mainstream algorithms, and the accuracy of the three attitude angles reaches the arc-second level.

GEOVISUALIZATION OF AERIAL PHOTOGRAMMETRIC FLIGHTS FOR DATA QUALITY ASSESSMENT

Spatiotemporal data visualization plays an important role for simulating the changes over time and representing dynamic geospatial phenomena. In aerial photogrammetry, image acquisition is the most important stage for obtaining high-quality products; and can be affected by various factors such as the weather and illumination conditions, imaging geometry, etc. 3D simulation of the aircraft trajectories at the planning stage helps the flight planners to make better decisions especially for unmanned aerial vehicle (UAV) missions in areas with mixed land use land cover, such as rugged topography, water bodies, restricted areas, etc.; since images with poor texture or large differences in scale may deteriorate the quality of the final products. In this study, a geovisualization approach for photogrammetric flights carried out with UAVs or airplane platforms was implemented using CesiumJS Virtual Globe. The measured flight trajectory parameters, such as image perspective centre coordinates and the camera rotations, the time of acquisition, and the interior orientation parameters (IOPs) of the camera were used for spatiotemporal visualization. In the developed approach, the EOPs and IOPs of the images were utilized to reconstruct the flight paths, the camera position, the footprints of the acquired images on the ground, and the rotation of the aircraft; and to present them on a 3D web environment precisely. The approach was demonstrated by using two case studies, one from a UAV flight mission and the other one from an airplane carried out with a large-format aerial camera.

Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston

Flood masks are among the most common remote sensing products, used for rapid crisis information and as input for hydraulic and impact models. Despite the high relevance of such products, vegetated and urban areas are still unreliably mapped and are sometimes even excluded from analysis. The information content of synthetic aperture radar (SAR) images is limited in these areas due to the side-looking imaging geometry of radar sensors and complex interactions of the microwave signal with trees and urban structures. Classification from SAR data can only be optimized to reduce false positives, but cannot avoid false negatives in areas that are essentially unobservable to the sensor, for example, due to radar shadows, layover, speckle and other effects. We therefore propose to treat satellite-based flood masks as intermediate products with true positives, and unlabeled cells instead of negatives. This corresponds to the input of a positive-unlabeled (PU) learning one-class classifier (OCC). Assuming that flood extent is at least partially explainable by topography, we present a novel procedure to estimate the true extent of the flood, given the initial mask, by using the satellite-based products as input to a PU OCC algorithm learned on topographic features. Additional rainfall data and distance to buildings had only minor effect on the models in our experiments. All three of the tested initial flood masks were considerably improved by the presented procedure, with obtainable increases in the overall κ score ranging from 0.2 for a high quality initial mask to 0.7 in the best case for a standard emergency response product. An assessment of κ for vegetated and urban areas separately shows that the performance in urban areas is still better when learning from a high quality initial mask.