The use of THz frequencies has enabled the opportunity to perform synthetic-aperture radar (SAR) imaging at the sub-mm level. It is of great interest for applications where high-resolution remote sensing in short range is required. However, with the increase of the operating frequencies from microwave to THz, the SAR image formation algorithms work with a larger amount of data and become more sensitive to phase errors that can be caused by insufficient signal sampling rate or physical factors that cause platform deviations. This motivates the use of fast image formation capable to handle phase errors. In this paper, we present the experimental results on the performance of the local backprojection (LBP) algorithm for processing THz SAR signals. The LBP algorithm has been tested with the real data in the frequency range 0.325-0.5 THz. The results demonstrate the efficiency of the LBP algorithm for the SAR scene reconstruction and highlight the necessity of the use of two times higher signal upsampling to achieve reconstruction accuracy similar to the global backprojection algorithm. 

One of the main challenges in Synthetic Aperture Radar (SAR) change detection involves using SAR images from different flight passes. Depending on the flight pass, objects have different specular reflections since the radar cross-sections of these objects can be totally different between passes. Then, it is common knowledge that the flight passes must be close to identical for conventional SAR change detection. Wavelength-resolution SAR refers to a SAR system with a spatial resolution approximately equal to the wavelength. This high relative resolution helps to stabilize the ground clutter in the SAR images. Consequently, the restricted requirement about identical flight passes for SAR change detection can be relaxed, and SAR change detection becomes possible with nonidentical passes. This paper shows that robust principal component analysis (RPCA) is efficient for change detection even using wavelength-resolution SAR images acquired with very different flight passes. It presents several SAR change detection experimental results using flight pass differences up to 95°. For slightly different passes, e.g., 5°, our method reached a false alarm rate (FAR) of approximately one false alarm per square kilometer for a probability of detection (PD) above 90%. In a particular setting, it achieves a PD of 97.5% for a FAR of 0.917 false alarms per square kilometer, even using SAR images acquired with nonidentical passes. 

The resolution of a synthetic aperture radar (SAR) system depends not only on the operating frequency range of a radar but also on the geometry. A radar system operating at MHz frequencies facilitates SAR resolution to the meter (m) level with a linear aperture. With the same radar system, the realization of the circular aperture can further enhance the SAR resolutions to the submeter level. For GHz SAR systems, the realization of circular apertures helps to reduce the resolution from the centimeter (cm) level to the millimeter (mm) level. In this paper, a resolution equation for SAR systems synthesizing circular aperture (CSAR) is introduced. The equation is derived from the backprojection integral for CSAR, its Fourier transform, and the method of stationary phase. The accuracy of the derived equation is enhanced with a correcting factor that is numerically calculated with a cubic interpolation. Therefore, the equation can provide a more accurate, analytical, and practical estimate of the spatial resolution that can be reached with a CSAR system than the ones available in the literature. The resolution equation introduced in this paper is verified with simulations of a MHz SAR system and a GHz SAR system. The equation is further verified by an experiment with a THz inverse SAR (ISAR) system. The radar utilized in the ISAR experiment is a D-band radar system mounted on an antenna positioner that supports circular movement.

The emerge of Terahertz (THz) radar systems allows the short-range applications such as material characterization, hyper accuracy localization at cm or even better level, scattering analysis of rough surfaces, and material defect detection. The paper presents the experimental results about material defect detection based on synthetic aperture radar (SAR) operating at THz frequencies. For the experiments, an electrical insulator is damaged causing the defects in the orders of mm and sub-mm. A SAR testbed at THz frequencies built with a vector network analyzer and a frequency extender in the frequency range 325-500 GHz is used to measure the electrical insulator in the form of SAR with a two-dimensional (2D) aperture. The damaged parts inside the electrical insulator can be observed clearly in the three-dimensional (3D) SAR image. This supports the material defect detection that enhances monitoring industrial production processes and controlling product quality. 

A solution for the three-dimensional (3D) hyper-accurate localization in indoor environment for mobile equipment problem can be based on radar systems. Mobile equipment with an integrated radar system is known as a joint radar-communication (JRC) system or a joint communication and sensing (JCAS) system. The paper proposes an approach for 3D hyper-accurate localization in indoor environment without modifications of cellular network infrastructure. The simulations and experiments show the feasibility of the proposal. © 2024 IEEE.

This study presents a new back-propagation (BP) method to determine the vertical location of ionospheric irregularities using GNSS Radio Occultation (GNSS-RO) signals. GNSS-RO employs signals from GNSS satellites intercepted by Low Earth Orbit (LEO) satellites to gather data about different atmospheric layers, e.g., the ionosphere, which are crucial for weather prediction and studying ionospheric dynamics. The BP method involves computing diffractive integrals along the LEO path to identify disturbances such as sporadic E-layer clouds and equatorial plasma bubbles (EPBs). By effectively unwinding diffraction and multipath effects, the method pinpoints regions with minimal amplitude disturbance, indicating the location of ionospheric irregularities along the ray path. Beside estimates along the horizontal axis, case studies demonstrate the new method's capabilities in locating and estimating the vertical extent of these irregularities, showing its potential to enhance ionospheric modelling and forecasting. Results achieved show consistency with previous publications on the topic as well as methodologies used to locate ionospheric irregularities, allowing the presented method a better picture of the ionospheric irregularity.

Wavelength-resolution (WR) synthetic aperture radar (SAR) change detection (CD) has been used to detect concealed targets in forestry areas. However, most proposed methods are generally based on matrix or vector analyses and, therefore, do not exploit information embedded in multidimensional data. In this letter, a CD method for WR SAR image stacks based on tensor robust principal component analysis (TRPCA) is proposed. The proposed CD method used the new tensor nuclear norm induced by the definition of the tensor-tensor product to exploit temporal and spatial information contained in the image stack. To assess the performance of the proposed method, we considered SAR images obtained by the very high frequency (VHF) WR CARABAS-II SAR system. Experiments for three different stack sizes show that a significant performance gain can be achieved when large image stacks are considered. The proposed CD method performs better in terms of probability of detection (PD) and false alarm rate (FAR) than the other five CD methods in VHF WR SAR images, including one based on matrix robust principal component analysis (RPCA). In a particular setting, it achieves a PD of 99% and a FAR of 0.028 false alarms per km2. Authors

This study addresses the issue of black-grass, a herbicide-resistant weed that threatens wheat yields in Western Europe, through the use of high- resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation in precision agriculture. We mitigate challenges such as the need for large labeled datasets and environmental variability by employing synthetic data augmentations in training a Mask R-CNN model. Using a minimal dataset of 43 black-grass and 12 wheat field images, we achieved a 37% increase in Area Under the Curve (AUC) over the non-augmented baseline, with scaling as the most effective augmentation. The best model attained a recall of 53% at a precision of 64%, offering a promising approach for future precision agriculture applications. © NLDL 2024. All rights reserved.

In this paper, we present experimental results on SAR image co-registration. The simple-coregistration method does not requires information on coordinates of flights, ground scene and so on. Two reference samples in each SAR image that can be easily selected are what the method requires. The method is based on re-sampling SAR images with respect to a selected image plane. The re-sampling was performed based on 2-D fast Fourier transform and cubic spline 2D interpolation. The experimental results in multitemporal data with ICEYE satellite data show an increase in the coherence magnitude between the images after the co-registration method. Namely, the coherence increases about three times after co-registration in a particular setting. 

This work investigates the impact of the neural networks architecture when performing fingerprint recognition. Three networks are studied; a Triplet network and two Siamese networks. They are evaluated on datasets with specified amounts of relative translation between fingerprints. The results show that the Siamese model based on contrastive loss performed best in all evaluated metrics. Moreover, the results indicate that the network with a categorical scheme performed inferior to the other models, especially in recognizing images with high confidence. The Equal Error Rate (EER) of the best model ranged between 4%−11% which was on average 6.5 percentage points lower than the categorical schemed model. When increasing the translation between images, the networks were predominantly affected once the translation reached a fourth of the image. Our work concludes that architectures designed to cluster data have an advantage when designing an authentication system based on neural networks.