Renato Machado

Urban tunneling projects pose significant geotechnical challenges, especially in densely populated regions where heterogeneous subsurface conditions increase the risk of ground displacements. Monitoring these displacements is therefore essential to ensure infrastructure safety and minimize potential impacts on surrounding communities. Traditional geotechnical monitoring methods, such as ground-based sensors, achieve sub-millimeter precision with high temporal resolution but are limited in spatial coverage and incur high operational costs. In addition, they may interrupt construction activities and disturb neighborhoods, restricting their deployment to areas directly above critical infrastructure. This limitation often results in incomplete datasets and contributes to legal disputes over alleged tunneling-induced damage. This work investigates the application of Persistent Scatterer Interferometry (PSI) as a complementary technique for settlement monitoring in the expansion of São Paulo Metro Line 2. This large-scale project is expected to benefit approximately 1.2 million people, with a public investment of R$ 13.4 billion. The construction, which began in 2021, is being excavated in Paleogene sediments of the São Paulo and Resende formations of the São Paulo Basin, as well as Quaternary alluvial deposits. Owing to the rift-related tectonic heritage that originated this basin, the local geology is highly heterogeneous, which may result in differential settlement and further reinforces the need for comprehensive monitoring strategies.Using high-resolution X-band images (1m resolution) from the ICEYE microsatellite constellation, this study employs SARPROZ to evaluate the dataset's coherence and baseline characteristics and assesses the potential of PSI for wide-area monitoring in a dense urban environment. The preliminary results demonstrated the significant challenges inherent in processing high-resolution X-band data from emerging constellations. Specifically, the large perpendicular baselines present in the dataset increased the sensitivity to topographic phase errors and geometric decorrelation, which, combined with strong atmospheric phase screen (APS) effects, hindered the isolation of the deformation signal through conventional linear phase modeling. These findings highlight the critical role of baseline optimization and advanced APS mitigation strategies when applying PSI to microsatellite constellations in tropical urban settings. Despite these constraints, this study provides valuable insights into the feasibility of integrating satellite-based SAR data with in situ monitoring for tunneling projects, offering a pathway toward more comprehensive, reliable, and cost-effective settlement monitoring frameworks to support informed decision-making in large-scale infrastructure development.

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.

Comparing images captured by disparate sensors is a common challenge in remote sensing. This requires image translation—converting imagery from one sensor domain to another while preserving the original content. Denoising diffusion implicit models (DDIM) are potential state-of-the-art solutions for such domain translation due to their proven superiority in multiple image-to-image translation tasks in computer vision. However, these models struggle with reproducing radiometric features of large-scale multipatch imagery, resulting in inconsistencies across the full image. This renders downstream tasks like heterogeneous change detection impractical. To overcome these limitations, we propose a method that leverages denoising diffusion for effective multisensor optical image translation over large areas. Our approach super-resolves large-scale low spatial resolution images into high-resolution equivalents from disparate optical sensors, ensuring uniformity across hundreds of patches. Our contributions lie in new forward and reverse diffusion processes that address the challenges of large-scale image translation. Extensive experiments using paired Sentinel-II (10 m) and Planet Dove (3 m) images demonstrate that our approach provides precise domain adaptation, preserving image content while improving radiometric accuracy and feature representation. A thorough image quality assessment and comparisons with the standard DDIM framework and five other leading methods are presented. We reach a mean learned perceptual image patch similarity of 0.1884 and a Fréchet Inception Distance of 45.64, expressively outperforming all compared methods, including DDIM, ShuffleMixer, and SwinIR. The usefulness of our approach is further demonstrated in two Heterogeneous Change Detection tasks.

In the context of synthetic aperture radar (SAR) systems, the Gamma distribution stands out as a strong contender for clutter modeling. The cell-averaging constant false alarm rate (CA-CFAR) detector has frequently been employed as a well-balanced detection technique, ensuring a blend of high performance and comparatively low complexity. In this study, we evaluate, in an exact manner, the CA-CFAR detector’s performance in a SAR system, assuming the presence of correlated Gamma-distributed clutter. To this aim, we derive novel exact expressions for the probability of detection (PD) and the probability of false alarm (PFA), simplifying their runtime evaluations without depending on specific mathematical software. The independent and identically distributed (IID) and independent not identically distributed (INID) cases are also analyzed. In particular, for the IID case, our derived PD and PFA expressions are given in closed form. Our analytical findings are validated through Monte Carlo simulations.

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 km ² .

The recently proposed frequency scanning (F-Scan) technique, together with the new International Telecommunication Union (ITU) allocation of 1200 MHz in X-band, enables the improvement of important performance parameters of synthetic aperture radar (SAR) acquisitions, such as the swath width, the signal-to-noise ratio, and the range ambiguity-to-signal ratio. The concurrent imaging technique, in turn, increases the flexibility of the radar system by allowing the simultaneous imaging of two or more areas with independent imaging modes. The integration of both techniques, therefore, enables the already valuable concurrent mode to achieve much better performance. Furthermore, motivated by the high-resolution wide-swath (HRWS) mission proposal in X-band, the displaced phase center antenna (DPCA) technique is considered to improve the azimuth resolution by using multiple receive (Rx) channels in azimuth. In this article, we discuss the design and performance of a new concurrent imaging mode that is enhanced by the F-Scan and DPCA techniques to make simultaneous imaging more flexible and powerful. Special attention is given to timing, range ambiguities, and availability aspects. The capability to simultaneously acquire two high-quality images with noteworthy flexibility is innovative and of great value, not only in daily operational applications but especially in extraordinary and crisis situations.

We propose an algorithm based on Information Theory to detect changes in Ultra-Wideband (UWB) Very-High Frequency (VHF) Synthetic Aperture Radar (SAR) images with high performance and low complexity. Our algorithm models the clutter-plus-noise using six different distributions and computes a scalar statistic for each pixel based on a multi-temporal stack of images. With this statistic, it is then possible to apply hypothesis testing and classification methods to infer the occurrence of a change. In this context, we derive expressions necessary for the entropy-based statistics, including the entropy variance for the Weibull and Rice distributions. We also evaluate the computational time complexity of the algorithm for each distribution studied. Furthermore, a masking strategy is used to reduce false alarms significantly. We show that the mask mapping assumptions are mild in scenarios with stacks of images, allowing its use in many scenarios. Our algorithm achieves a false alarm rate (FAR) of 0.08 and a probability of detection (PD) of 100%, outperforming existing methods on the CARABAS II data set.