Airborne SAR Radiometric Calibration Based on Improved Sliding Window Integral Method

To verify the performance of the high-resolution fully polarimetric synthetic aperture radar (SAR) sensor carried by the Xinzhou 60 remote-sensing aircraft, we used corner reflectors to calibrate the acquired data. The target mechanism in high-resolution SAR images is more complex than it is in low-resolution SAR images, the impact of the point target pointing error on the calibration results is more obvious, and the target echo signal of high-resolution images is more easily affected by speckle noise; thus, more accurate extraction of the point target position and the response energy is required. To solve this problem, this paper introduces image context information and proposes a method to precisely determine the integration region of the corner reflector using sliding windows based on the integral method. The validation indicates that the fully polarimetric SAR sensor on the Xinzhou 60 remote-sensing aircraft can accurately reflect the radiometric characteristics of the ground features and that the integral method can obtain more stable results than the peak method. The sliding window allows the position of the point target to be determined more accurately, and the response energy extracted from the image via the integral method is closer to the theoretical value, which means that the high-resolution SAR system can achieve a higher radiometric calibration accuracy. Additionally, cross-validation reveals that the airborne SAR images have similar quality levels to Sentinel-1A and Gaofen-3 images.

Unified Approach For Multiscale Radar Cross-Section Measurement by Synthetic Aperture Imaging

<div><div>*This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</div></div><div><br></div>In this study, we present an improved and unified approach for image-based radar cross-section (RCS) measurement by 2-D synthetic aperture radar (SAR) imaging with an arbitrary curved antenna scanning trajectory. Because RCS is a quantity defined in the far-field distance of an object under test, direct RCS measurement of an electrically large target is often infeasible owing to the spatial limitation of the measurement facility. The method proposed in this study belongs to the class of techniques referred to as the image-based near-field to far-field transformation (NFFFT) to convert the near-field data of scattering experiment into the far-field RCS. In a previous study, we have developed an NFFFT based on 3-D SAR imaging with an arbitrary antenna scanning surface. However, the previous approach is only applicable to the surface scanning which is impossible for a certain case such as measurement using airborne SAR or vehicle-borne SAR. Therefore, one requires an alternative method that can accommodate an arbitrary scanning curve, which is the subject of this study. We derive a generalized correction factor for image-based NFFFT which is designed to ensure the integral transformation in the image reconstruction process be self-consistent for electrically small scatterers. We provide a series of numerical simulations, an indoor experiment, and an airborne SAR experiment to validate that the proposed scheme can be utilized for various situations ranging from near-field to far-field distance.

Unified Approach For Multiscale Radar Cross-Section Measurement by Synthetic Aperture Imaging

<div><div>*This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</div></div><div><br></div>In this study, we present an improved and unified approach for image-based radar cross-section (RCS) measurement by 2-D synthetic aperture radar (SAR) imaging with an arbitrary curved antenna scanning trajectory. Because RCS is a quantity defined in the far-field distance of an object under test, direct RCS measurement of an electrically large target is often infeasible owing to the spatial limitation of the measurement facility. The method proposed in this study belongs to the class of techniques referred to as the image-based near-field to far-field transformation (NFFFT) to convert the near-field data of scattering experiment into the far-field RCS. In a previous study, we have developed an NFFFT based on 3-D SAR imaging with an arbitrary antenna scanning surface. However, the previous approach is only applicable to the surface scanning which is impossible for a certain case such as measurement using airborne SAR or vehicle-borne SAR. Therefore, one requires an alternative method that can accommodate an arbitrary scanning curve, which is the subject of this study. We derive a generalized correction factor for image-based NFFFT which is designed to ensure the integral transformation in the image reconstruction process be self-consistent for electrically small scatterers. We provide a series of numerical simulations, an indoor experiment, and an airborne SAR experiment to validate that the proposed scheme can be utilized for various situations ranging from near-field to far-field distance.

Enhancing Coherence Images for Coherent Change Detection: An Example on Vehicle Tracks in Airborne SAR Images

In Synthetic Aperture Radar (SAR) interferometry, one of the most widely used measures for the quality of the interferometric phase is coherence. However, in favorable conditions coherence can also be used to detect subtle changes on the ground, which are not visible in the amplitude images. For such applications, i.e., coherent change detection, it is important to have a good contrast between the unchanged (high-coherence) parts of the scene and the changed (low-coherence) parts. In this paper, an algorithm is introduced that aims at enhancing this contrast. The enhancement is achieved by a combination of careful filtering of the amplitude images and the interferometric phase image. The algorithm is applied to an airborne interferometric SAR image pair recorded by the SmartRadar experimental sensor of Hensoldt Sensors GmbH. The data were recorded during a measurement campaign over the Bann B installations of POLYGONE Range in southern Rhineland-Palatinate (Germany), with a time gap of approximately four hours between the overflights. In-between the overflights, several vehicles were moved on the site and the goal of this work is to enhance the coherence image such that the tracks of these vehicles can be detected as completely as possible in an automated way. Several coherence estimation schemes found in the literature are explored for the enhancement, as well as several commonly used speckle filters. The results of these filtering steps are evaluated visually and quantitatively, showing that the mean gray-level difference between the low-coherence tracks and their high-coherence surroundings could be enhanced by at least 28%. Line extraction is then applied to the best enhancement. The results show that the tracks can be detected much more completely using the coherence contrast enhancement scheme proposed in this paper.

Mapping Urban Excavation Induced Deformation in 3D via Multiplatform InSAR Time-Series

Excavation of a subway station and rail crossover cavern in downtown Los Angeles, California, USA, induced over 1.8 cm of surface settlement between June 2018 and February 2019 as measured by a ground-based monitoring system. Point measurements of surface deformation above the excavation were extracted by applying Interferometric Synthetic Aperture Radar (InSAR) time-series analyses to data from multiple sensors with different wavelengths. These sensors include C-band Sentinel-1, X-band COSMO-SkyMed, and L-band Uninhabited Aerial Vehicle SAR (UAVSAR). The InSAR time-series point measurements were interpolated to continuous distribution surfaces, weighted by distance, and entered into the Minimum-Acceleration (MinA) algorithm to calculate 3D displacement values. This dataset, composed of satellite and airborne SAR data from X, C, and L band sensors, revealed previously unidentified deformation surrounding the 2nd Street and Broadway Subway Station and the adjacent rail crossover cavern, with maximum vertical and horizontal deformations reaching 2.5 cm and 1.7 cm, respectively. In addition, the analysis shows that airborne SAR data with alternative viewing geometries to traditional polar-orbiting SAR satellites can be used to constrain horizontal displacements in the North-South direction while maintaining agreement with ground-based data.

Airborne SnowSAR data at X- and Ku- bands over boreal forest, alpine and tundra snow cover

The European Space Agency SnowSAR instrument is a side looking, dual polarized (VV/VH), X/Ku band synthetic aperture radar (SAR), operable from a small aircraft. Between 2010 and 2013, the instrument was deployed at several sites in Northern Finland, Austrian Alps, and northern Canada. The purpose of the airborne campaigns was to measure the backscattering properties of snow-covered terrain to support the development of snow water equivalent retrieval techniques using SAR. SnowSAR was deployed in Sodankylä, Northern Finland for a single flight mission in March 2011 and twelve missions at two sites (tundra and boreal forest) in the winter of 2011–2012. Over the Austrian Alps, three flight missions were performed between November 2012 and February 2013 over three sites located in different elevation zones, representing a montane valley, Alpine tundra, and a glacier environment. In Canada, a total of two missions were flown in March and April 2013, over sites in the Trail Valley Creek watershed, Northwest Territories, representative of the tundra snow regime. This paper introduces the airborne SAR data, as well as coincident in situ information on land cover, vegetation and snow properties. To facilitate easy access to the data record the datasets described here are deposited in a permanent data repository (https://doi.pangaea.de/10.1594/PANGAEA.933255; Lemmetyinen et al., 2021). A temporary link to access the data without login information is provided for reviewers of this manuscript: https://www.pangaea.de/tok/e8c562c3c8a15ac34daa83d00c76fcb347330884.

Airborne SAR Autofocus Based on Blurry Imagery Classification

Existing airborne SAR autofocus methods can be classified as parametric and non-parametric. Generally, non-parametric methods, such as the widely used phase gradient autofocus (PGA) algorithm, are only suitable for scenes with many dominant point targets, while the parametric ones are suitable for all types of scenes, in theory, but their efficiency is generally low. In practice, whether many dominant point targets are present in the scene is usually unknown, so determining what kind of algorithm should be selected is not straightforward. To solve this issue, this article proposes an airborne SAR autofocus approach combined with blurry imagery classification to improve the autofocus efficiency for ensuring autofocus precision. In this approach, we embed the blurry imagery classification based on a typical VGGNet in a deep learning community into the traditional autofocus framework as a preprocessing step before autofocus processing to analyze whether dominant point targets are present in the scene. If many dominant point targets are present in the scene, the non-parametric method is used for autofocus processing. Otherwise, the parametric one is adopted. Therefore, the advantage of the proposed approach is the automatic batch processing of all kinds of airborne measured data.

Indoor Experiments of Bistatic/Multistatic GB-SAR with One-Stationary and One-Moving Antennae

Ground-based synthetic aperture radar (GB-SAR) is a useful tool to simulate advanced SAR systems with its flexibility on RF system and SAR configuration. This paper reports an indoor experiment of bistatic/multistatic GB-SAR operated in Ku-band with two antennae: one antenna was stationary on the ground and the other was moving along a linear rail. Multiple bistatic GB-SAR images were taken with various stationary antenna positions, and then averaged to simulate a multistatic GB-SAR configuration composed of a moving Tx antenna along a rail and multiple stationary Rx antennae with various viewing angles. This configuration simulates the use of a spaceborne/airborne SAR system as a transmitting antenna and multiple ground-based stationary antennae as receiving antennae to obtain omni-directional scattering images. This SAR geometry with one-stationary and one-moving antennae configuration was analyzed and a time-domain SAR focusing algorithm was adjusted to this geometry. Being stationary for one antenna, the Doppler rate was analyzed to be half of the monostatic case, and the azimuth resolution was doubled. Image quality was enhanced by identifying and reducing azimuth ambiguity. By averaging multiple bistatic images from various stationary antenna positions, a multistatic GB-SAR image was achieved to have better image swath and reduced speckle noise.