InSAR time-series results of the 2025 Santorini unrest, using Sentinel-1A data

In Japan, dikes along rivers are required by law to be managed by surveying them periodically at intervals of no more than 200 m along their length. Because Japan has so many dikes to survey, an efficient surveying approach is needed. This paper presents the results and accuracy of dike elevation estimation using multi-temporal synthetic aperture radar (SAR) images. First, the displacement along the radar line-of-sight direction was calculated by applying persistent-scatterer interferometry to SAR images observed on ascending and descending orbits. The least-squares method was then applied to those results, and the east–west and vertical displacement velocities were estimated by assuming that no north–south displacement occurred. Finally, the displacement velocity was multiplied by a chosen time period, and the elevation at the end of that time period was obtained by adding the known initial elevation. The experimental results show that the root-mean-square error of the elevation of the dike crown was 5.3 cm whereas that of the dike slope exceeded 25 cm. It is concluded that the presented approach is effective for monitoring the dike crown, and the accuracy of the elevation of the dike slope is expected to improve.

Matter of discussion in this Ph. D. thesis is SAR (Synthetic Aperture Radar) image denoising. Main elements of innovation are the introduction of SAR-BM3D, a denoising algorithm optimized for SAR data, and the introduction of a benchmark which enables the objective performance comparison of SAR denoising algorithms on simulated canonical SAR images. In the first part of the thesis, basic concepts on SAR images are introduced, with special emphasis on its peculiar multiplicative noise, called speckle. A description of key ideas and tools of denoising techniques known in literature then follows. After introducing the basic elements of SAR data processing, the main statistical features of SAR images are described, and it is clarified in which context the denoising techniques operate. Techniques are then classified as those that follow the homomorphic approach, where the multiplicative noise is turned into additive noise through a logarithmic transform, and those that take explicitly into account the multiplicative nature of noise. Afterwards, it is described how the introduction of the wavelet transform has brought new ideas into SAR image denoising and how the non-local filtering strategy, originally proposed in the AWGN field, has provided relevant results also in the application to SAR. In this context, the novel SAR-BM3D algorithm is introduced which, starting from key elements of wavelet-based and non-local filtering implemented in BM3D, optimizes the elaboration for SAR data, following a non-homomorphic approach. A very detailed experimental analysis on simulated SAR images, obtained as optical images corrupted by artificial speckle, has been performed: results proved the SAR-BM3D algorithm to outperform traditional approaches, both in terms of PSNR and visual inspection. Due to the well-known difficulties of evaluating the performance of denoising techniques on real SAR images, a workaround has been proposed. Rather than resorting to images corrupted by artificial speckle, a physical SAR simulator, SARAS (developed by the remote-sensing group of the Federico II University of Naples) has been used to generate a set of canonical benchmark SAR scenes. The main advantage of SARAS images is the availability of both the noisy and clean versions of the images, the latter acting as a reference to objectively evaluate the performances of different algorithms. We have shown in detail the procedure which leads to a definition of an objective criterion to compare results provided by different algorithms when working on real SAR images. For this purpose, different test cases have been selected and specific measures, suitable for the various scenes have been proposed for the characterization. At the end of the thesis, open issues are pointed out and future research is outlined.

Purpose Movement technique and velocity are very important in pole vaulting. This study aims to analyze the technique and velocity differences between successful and unsuccessful jumps. Material and methods This is quantitative research with a comparative design. There were 8 Male Collegiate Pole Vault Athletes (20 ± 1,389 years old) selected as research samples using a purposive sampling method. The kinematic analysis was conducted to analyze the technique of horizontal and vertical movement. The variables were observed in last four steps including distance, time, and velocity per step in the horizontal movement. The vertical movement variables were velocity and height of the approach run, plant, take off, swing up, turn, and fly away phases. The kinematics analysis of vertical and horizontal movement were analyzed by using video and Kinovea 0.9.5. The descriptive, normality, and comparative tests were analyzed by using SPSS 26. Results The data were mostly in the normal distribution. The parametric test was conducted to test the significantly different and non-parametric tests for the unnormal data distribution. A significant difference was observed between successful and unsuccessful jumps in both horizontal and vertical movements. In the horizontal movement, the difference is found in the velocity of the last step (p-value < 0.05). The average of successful jump velocity ( 7.480 ± 0.166) was faster than the unsuccessful jump (6.430 ± 1.999). Velocity differences in the vertical movement were found in the plant position, take-off position, and swing up position (p-value < 0.05). Conclusions The difference in velocity in the horizontal movement has the potential to support the velocity and height of the vertical movement.

The high-resolution synthetic aperture radar (SAR) images usually contain inhomogeneous coherent speckle noises. For the high-resolution SAR image segmentation with such noises, the conventional methods based on pulse coupled neural networks (PCNN) have to face heavy parameters with a low efficiency. In order to solve the problems, this paper proposes a novel SAR image segmentation algorithm based on non-subsampling Contourlet transform (NSCT) denoising and quantum immune genetic algorithm (QIGA) improved PCNN models. The proposed method first denoising the SAR images for a pre-processing based on NSCT. Then, by using the QIGA to select parameters for the PCNN models, such models self-adaptively select the suitable parameters for segmentation of SAR images with different scenes. This method decreases the number of parameters in the PCNN models and improves the efficiency of PCNN models. At last, by using the optimal threshold to binary the segmented SAR images, the small objects and large scales from the original SAR images will be segmented. To validate the feasibility and effectiveness of the proposed algorithm, four different comparable experiments are applied to validate the proposed algorithm. Experimental results have shown that NSCT pre-processing has a better performance for coherent speckle noises suppression, and QIGA-PCNN model based on denoised SAR images has an obvious segmentation performance improvement on region consistency and region contrast than state-of-the-arts methods. Besides, the segmentation efficiency is also improved than conventional PCNN model, and the level of time complexity meets the state-of-the-arts methods. Our proposed NSCT+QIGA-PCNN model can be used for small object segmentation and large scale segmentation in high-resolution SAR images. The segmented results will be further used for object classification and recognition, regions of interest extraction, and moving object detection and tracking.

In this thesis the modeling of SAR (Synthetic Aperture Radar) images of natural surfaces described via fractal models is dealt with. A complete theoretical forward model linking the parameters describing the scene observed by the sensor to the stochastic characterization of the relevant SAR image is provided. The inverse problem is treated as well: a SAR image post-processing able to automatically retrieve - operating on an amplitude single SAR image - the fractal parameters of the scene, is presented. The developed imaging model is based on sound geometrical and electromagnetic models that are combined according to the SAR impulse response function. The power spectral densities of appropriate cuts of the SAR image are evaluated in closed form in terms of the surface fractal parameters. The theoretical results are here conceptually assessed, analytically derived, graphically validated and numerically verified. Moreover, based on the inversion of the forward theoretical model, an innovative SAR image post-processing for the fractal parameters estimation is implemented. It is firstly tested on simulated SAR images, then it is applied to different types of new generation (i.e. high resolution) SAR images. The generated fractal maps show themselves to be very useful for a wide range of application, e.g. prevention and monitoring of environmental disasters, geodynamic processes interpretation, land classification, rural planning, and so on.

ABSTRACTA feature extraction algorithm is proposed to quantitatively assess the condition of intact and damaged carbon fiber reinforced polymer (CFRP)-wrapped concrete cylinders using synthetic aperture radar (SAR) images. The proposed algorithm converts SAR images into a simplified representation, based on the shape, size, and amplitude of SAR images. In this approach, the shape of scatterers in a SAR image is characterized by average Gaussian curvature (K), area ratio (R), and SAR amplitude (I), and is represented by a K-R-I curve. SAR images of intact and damaged CFRP-wrapped concrete cylinders were generated by a stripmap SAR imaging radar system (10.5 GHz) at various inspection angles (0°, 15°, 25°, 30°, 45°, and 60°). From our experimental result, it is found that the K-R-I representation of SAR images is capable of distinguishing damaged SAR images from intact ones at different inspection angles. Quantitative dissimilarity between the K-R-I curves of intact and damaged specimens is assessed by coefficient of correlation and compared with the signal-to-noise ratio (SNR) of SAR images. It is found that the dissimilarity of K-R-I curves is closely related to the SNR of SAR images, demonstrating the feasibility and potential of the proposed K-R-I representation.

This paper investigates the effect of the application of Spatially Variant Apodization techniques to SAR images on the statistical properties of the apodized radar signal and on the capability of extracting information from apodized SAR images. After deriving the statistical model of the apodized image (in terms of both probability density function and moments) two new classification schemes of homogeneous regions with different radar cross section in apodized SAR images are obtained. The performance of the new schemes are deeply investigated and compared with the performance achievable by Maximum Likelihood classification schemes developed under the assumption of Gaussian statistics and applied to the original images and to the apodized images. The performance analysis shows that the new schemes maintain the information extraction capabilities while at the same time allowing the sidelobe level to be reduced and the mainlobe resolution to be preserved. Radar imaging often requires sidelobe control: as well known both linear and non linear techniques can be used to reduce the sidelobe level. Linear techniques are based on the use of amplitude weighting function (frequency domain) before the final Fourier transform: in this case the reduction in sidelobe level is obtained at the expense of the mainlobe width with a loss in resolution. Apodization techniques are non linear techniques which have been proposed to reduce sidelobe level while preserving mainlobe resolution. This is of particular importance especially in range dimension where the high resolution is provided by the transmitted bandwidth, usually limited by technological or regulations constraints. Due to their non linear behavior, it is of great importance to understand the impact of the apodization techniques on the quality of SAR (Synthetic Aperture Radar) images in terms of capability of extracting information from apodized SAR images. In this paper we focus on Spatially Variant Apodization (SVA), (1), applied separately to the in-phase (I) and quadrature (Q) components in the range dimension and analyze the impact of SVA on SAR image statistical properties and on information extraction in terms of classification of homogeneous regions with different radar cross section. probability density function (PDF) of the apodized random variable (r.v.) and (ii) the moments of generic order of the apodized r.v.. On this basis the impact of apodization techniques on the following interpretation of SAR images is analyzed in Section 3. In particular we aim at understanding the impact of SVA on the classification of homogeneous regions in SAR images. We show that the use of classical ML (Maximum Likelihood) classifier, developed under the assumption of a Gaussian statistics, results in strong losses when applied to apodized SAR images; to cope with this problem, by using the knowledge of the statistical properties of the apodized SAR images, we derive two new classification schemes in order to maintain the information extraction capabilities while at the same time allowing the sidelobe level to be reduced and the mainlobe resolution to be preserved. Finally, some conclusions are drawn in Section 4.

In this paper we describe a fusion approach for automatic object extraction from multi-aspect SAR images. Before fusion the uncertainty of each extracted object is assessed by means of Bayesian probability theory. The assessment is performed on attribute-level and is based on predefined probability density functions learned from training data. I. INTRODUCTION Automatic extraction of man-made objects from synthetic aperture radar (SAR) images is regarded as a complicated task. Compared to optical image acquisition, SAR system is an active system and can operate during day and night. It is also nearly weather-independent and, moreover, during bad weather conditions, SAR is the only operational system available today. Extraction of man-made objects from SAR images therefore offers a suitable complement or alternative to object extraction from optical images. The recent development of new high resolution SAR systems offers new potential for automatic object extraction. Satellite SAR images up to 1 m resolution will soon be available by the launch of the German satellite TerraSAR-X (1). Airborne images already provide resolution up to 1 decimetre (2). However, the improved resolution does not automatically make automatic object extraction easier, yet it faces new challenges. Especially in urban areas, the complexity arises through dominant scattering caused by building structures, traffic signs and metallic objects in cities. These bright features hinder important extractable features. The inevitable consequences of the side-looking geometry of SAR, occlusions caused by shadow- and layover effects, is present in forestry areas as well as in built-up areas. In urban areas, the best results for the visibility of roads are obtained, when the illumination direction coincide with the main road orientations (3). Preliminary work has shown that the usage of SAR images illuminated from different directions (i.e. multi- aspect images) improves the road extraction results. This has been tested both for real and simulated SAR scenes (4)(5). Multi-aspect SAR images has appeared to be an interesting topic for automatic building extraction as well (6). In this article we present a fusion concept for object extraction based on a Bayesian statistical approach, which incorporates both global context and sensor geometry. The fusion will be implemented in a road extraction approach, (Sect. II), but can as well be applied for other man-made objects. The main focus of this paper is the proposed fusion module, which is explained in Sect. III. Some intermediate results of an uncertainty assessment of line segments based on a training step and global context are discussed in Sect IV. II. ROAD EXTRACTION SYSTEM The extraction of roads from SAR images is based on an already existing road extraction approach (7), which was originally designed for optical images with a ground pixel size of about 2m (8). The first step consists of line extraction using Steger's differential geometry approach (9), which is followed by a smoothening and splitting step. By applying explicit knowledge about roads, the line segments are evaluated according to their attributes such as width, length, curvature, etc. The evaluation is performed within the fuzzy theory. A weighted graph of the evaluated road segments is constructed. For the extraction of the roads from the graph, supplementary road segments are introduced and seed points are defined. Best- valued road segments serve as seed points, which are connected by an optimal path search through the graph. The novelty presented in this paper refers on one hand to the adoption of the fusion module to multi-aspect SAR images and on the other hand to a probabilistic formulation of the fusion problem instead of using fuzzy-functions.

Synthetic aperture radar (SAR) imaging is very sensitive to direction, so the information that SAR images contain is often not completely same from different directions. The information obtained from multi-directions must be more abundant and more accurate than that of from a single direction in a SAR image. The Contourlet transform is a multi-scale geometric analysis theory, holding many advantages for signal processing, such as multi-resolution, multi-direction and anisotropy; therefore, it is in favor of extracting different direction information for SAR images. According to the directional sensitivity of SAR imaging and the characteristics of multi-scale and multi direction to the Contourlet transform, this paper proposed a new SAR image change detection method based on Contourlet transform, called CTCD algorithm. Using the multi-direction characteristic of Contourlet transform, the CTCD method can get more accurate changed information for multi-temporal SAR images. The practical SAR image data is employed to test the CTCD algorithm and results show that the CTCD algorithm is a feasible change detection algorithm for multi-temporal SAR images, and it can obtain more abundant and more accurate information than the direct difference change detection (DDCD) algorithm.

Deep-learning-based target recognition in synthetic aperture radar (SAR) images has been actively studied in recent years. However, it is very costly to collect large numbers of labeled SAR images, especially measured SAR target images of various classes, to train high-performance classification networks. To solve the problem of insufficient SAR data, electromagnetic computational tools have often been developed and used to synthesize the measured SAR target images from data modeling. However, despite the use of sophisticated SAR image modeling, there is a large domain gap between synthetic SAR images and measured images such that networks trained with synthetic SAR images tend to show poor classification performance when tested on measured SAR target images. In this paper, we propose a novel transformer-based synthetic-to-measured SAR target image translation network, referred to as SAR-SMT Net, to bridge the gap between synthetic and measured SAR target images. SAR-SMT Net takes synthetic SAR target images as input and estimates the latent representational features of their corresponding measured SAR images to faithfully adjust the global context and scattering characteristics of the input synthetic SAR target images to the corresponding measured SAR values. In addition, we propose five challenging experimental scenarios that can validate SAR image translation performance outcomes. Experimentally, SAR-SMT Net as proposed here outperforms previous state-of-the-art methods in the experiment scenarios, demonstrating feasible generalization ability when used to translate synthetic SAR target images into their corresponding measured SAR target images with a high level of fidelity, even for unseen target classes at unseen azimuth angles.

Expansive soils create severe problems for road construction and maintenance, building foundations, and agricultural and industrial operations. Quantification of soil movement under natural field conditions has received little attention and is not well understood. Horizontal and vertical movements were measured for 20 mo by surveying rods placed at various depths in field plots of Typic Chromuderts and Vertic Hapludalfs of the Mississippi Blackland Prairie. Vertical soil movement was a function of soil depth, soil water content, and rainfall. Greatest vertical soil movement occurred in the upper 50 cm of both soils, with a maximum of 27 mm in the Vertisol and 24 mm in the Alfisol. Soil swelling occurred in winter and shrinking occurred in late spring and summer. Horizontal soil movement was not related to soil depth, but was related to soil water content and rainfall. Maximum horizontal movement was 36 mm in the Vertisol and 20 mm in the Alfisol. Horizontal and vertical movements in expansive soils are reversible, dynamic processes under natural field conditions. Horizontal movement appears more dynamic than vertical movement, and it reflects short‐term response to precipitation events followed by drier periods. Vertical movement reflects long‐term precipitation‐evapotranspiration distribution and is probably related to movement along master slickensides.

Coastline detection in synthetic aperture radar (SAR) image is an important component for SAR image interpretation. However, because of speckling, SAR image coastline detection with high accuracy is far from resolved. In the paper, an improved method using multiscale wavelet-based despeckling and support vector machine (SVM) based classification is proposed for SAR image coastline detection. In the method, as a pre-process step, a multi-scale wavelet SAR image despeckling strategy is proposed to suppress the noise of SAR image. Base on despeckled SAR image, a novel method for water and non-water classification using circular-window Gray Level Co-occurrence Matrix (GLCM) and SVM is proposed. GLCM of circular-window is designed as texture feature of water and non-water areas for classification. Feature vector of eighteen dimensions is derived from GLCM and fed into a SVM-based classifier to get water region, the contour of water region is extracted as coastline. The experimental results using real SAR images demonstrate that the proposed approach has better performance compared with other ones. (6 pages)

The unavoidable noise often present in synthetic aperture radar (SAR) images, such as speckle noise, negatively impacts the subsequent processing of SAR images. Further, it is not easy to find an appropriate application for SAR images, given that the human visual system is sensitive to color and SAR images are gray. As a result, a noisy SAR image fusion method based on nonlocal matching and generative adversarial networks is presented in this paper. A nonlocal matching method is applied to processing source images into similar block groups in the pre-processing step. Then, adversarial networks are employed to generate a final noise-free fused SAR image block, where the generator aims to generate a noise-free SAR image block with color information, and the discriminator tries to increase the spatial resolution of the generated image block. This step ensures that the fused image block contains high resolution and color information at the same time. Finally, a fused image can be obtained by aggregating all the image blocks. By extensive comparative experiments on the SEN1–2 datasets and source images, it can be found that the proposed method not only has better fusion results but is also robust to image noise, indicating the superiority of the proposed noisy SAR image fusion method over the state-of-the-art methods.

Spaceborne SAR(synthetic aperture radar) imaging requires real-time processing of enormous amount of input data with limited power consumption. Designing advanced heterogeneous array processors is an effective way to meet the requirements of power constraints and real-time processing of application systems. To design an efficient SAR imaging processor, the on-chip data organization structure and access strategy are of critical importance. Taking the typical SAR imaging algorithm-chirp scaling algorithm-as the targeted algorithm, this paper analyzes the characteristics of each calculation stage engaged in the SAR imaging process, and extracts the data flow model of SAR imaging, and proposes a storage strategy of cross-region cross-placement and data sorting synchronization execution to ensure FFT/IFFT calculation pipelining parallel operation. The memory wall problem can be alleviated through on-chip multi-level data buffer structure, ensuring the sufficient data providing of the imaging calculation pipeline. Based on this memory organization and access strategy, the SAR imaging pipeline process that effectively supports FFT/IFFT and phase compensation operations is therefore optimized. The processor based on this storage strategy can realize the throughput of up to 115.2 GOPS, and the energy efficiency of up to 254 GOPS/W can be achieved by implementing 65 nm technology. Compared with conventional CPU+GPU acceleration solutions, the performance to power consumption ratio is increased by 63.4 times. The proposed architecture can not only improve the real-time performance, but also reduces the design complexity of the SAR imaging system, which facilitates excellent performance in tailoring and scalability, satisfying the practical needs of different SAR imaging platforms.

This paper proposes a speckle-suppression method for ocean internal solitary wave (ISW) synthetic aperture radar (SAR) images by using the curvelet transform. The band-shaped signatures of ocean ISWs in SAR images show obvious scale and directional characteristics. The curvelet transform possesses a very high scale and directional sensitivity. Therefore, the curvelet transform is very efficient in analyzing wave signals in SAR images. A noisy ocean ISW SAR image can be decomposed at different scales, directions, and positions using the curvelet transform. The information of the ISWs is centralized in the curvelet coefficients of certain directions under certain scales, whereas the speckle noise is distributed in every scale and direction. By manipulating the curvelet coefficients, the signals of the ISWs can be extracted from the noisy SAR image. Finally, the speckle noise is suppressed and the ISW feature is enhanced by adding the signals of the ISWs back to the original SAR image. Experiments demonstrate the effectiveness of this method.