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Rotating Synthetic Aperture (RSA) technology is one of the distinctly advantageous Earth geostationary orbit optical remote sensing technologies. However, the continuous rotation of the RSA system's rectangular primary mirror results in a discernible drop in resolution along the shorter side of the mirror. Additionally, the captured images exhibit periodic and time-varying characteristics. To improve the image quality to meet interpretation needs, we first delineate the imaging process of the rotating primary mirror and analyze the characteristics of image degradation based on the system's imaging mechanism. Then, we propose a dual super-resolution (SR) framework based on Swin Transformer and introduce a self-supervised learning method for jointly training the unified SR network using wavelet fusion. The self-supervised learning method effectively utilizes the spatiotemporal correlation of the information contained in images captured at different rotation directions of the rectangular pupil. Moreover, the attention mechanism in Transformer can adopt a global perspective and utilize content-based interactions between image content and attention weights to model strong long-range dependencies in remote sensing images. This approach significantly enhances image quality along the pupil's shorter side, consequently yielding superior results. Extensive digital and semi-physical imaging experiments, involving six aspect ratios of the primary mirror, demonstrate that our SR method surpasses state-of-the-art methods. The work in this paper can serve as a valuable reference for future space applications of the RSA technology. • Analyzing the imaging mechanisms of the rotating synthetic aperture system. • Self-supervised learning enhances the utilization of image internal information. • Designing a dual framework that can be trained jointly for image super resolution. • Interactions between image content and attention weights improve performance. • Performance is evaluated through digital and semi-physical experiments. [ABSTRACT FROM AUTHOR]

Lee, D.-H. and Choi, K., 2023. Feasibility study on the emergency acquisition of satellite images for maritime search and rescue. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 463-467. Charlotte (North Carolina), ISSN 0749-0208. With increasing maritime activities such as logistics and leisure, the frequency and scale of maritime accidents are growing. To minimize damage from maritime accidents and provide appropriate responses, the accident scenarios must be accurately predicted. To this end, maritime control and monitoring systems are required. Recently, remote sensing platforms such as drones, helicopters, and satellites have been increasingly used for accident monitoring. Images obtained by drones or helicopters have high resolution but small coverage because the platforms fly at a low altitude. In comparison, the images acquired from satellites, which operate in high orbits, have wide coverage and are thus more effective in monitoring vast maritime areas. However, because satellites capture images at a predetermined time according to the mission orbit, the observation period is limited. Therefore, a satellite imagery collection system based on satellites operating worldwide must be established to acquire images from the target time and place. This paper proposes an image collection system for acquiring satellite images of target areas in maritime accident scenarios. Based on a statistical analysis of historical maritime accident data, the main objects to be detected are selected, and the requirements are specified. Additionally, the spatial and revisit cycles and observation time of optical and synthetic aperture radar satellites are analyzed and an image collection system is established by selecting the satellites available for accident response. The proposed system can facilitate the use of satellite images to respond promptly to maritime accidents. [ABSTRACT FROM AUTHOR]

Change detection for synthetic aperture radar (SAR) images is a key process in many applications exploiting remote-sensing images. It is a challenging task due to the presence of speckle noise in SAR imaging. This article investigates the problem of change detection in multitemporal SAR images. Our motivation is to avoid using only one detector to measure the change level of different features which is usually considered by classical methods. In this article, we propose an unsupervised change detection approach based on frequency difference in wavelet domain and a modified fuzzy c-means (FCM) clustering algorithm. First, the proposed method extracts high-frequency and low-frequency components using wavelet transform, and then constructs highfrequency and low-frequency difference images using different detectors. Finally, inverse wavelet transform is carried out to obtain the final difference image. In addition, inspired by manifold structure constraint, we incorporate weighted local information into the FCM to reduce the influence of speckle noise. Experimental results performed on simulated and real SAR images show the effectiveness of the proposed method, in terms of detection performance, compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Targets of interest are different in various applications in which manmade targets, such as aircraft, ships, and buildings, are given more attention. Manmade target extraction methods using synthetic aperture radar (SAR) images are designed in response to various demands, which include civil uses, business purposes, and military industries. This plays an increasingly vital role in monitoring, military reconnaissance, and precision strikes. Achieving accurate and complete results through traditional methods is becoming more challenging because of the scattered complexity of polarization in polarimetric synthetic aperture radar (PolSAR) image. A multistage decision-based method is proposed composed of power decision, dominant scattering mechanism decision, and reflection symmetry decision. In addition, the theories of polarimetric contrast enhancement, generalized Y decomposition, and maximum eigenvalue ratio are applied to assist the decision. Fully PolSAR data are adopted to evaluate and verify the approach. Experimental results show that the method can achieve an effective result with a lower false alarm rate and clear contours. Finally, on this basis, a universal framework of change detection for manmade targets is presented as an application of our method. Two sets of measured data are also used to evaluate and verify the effectiveness of the change-detection algorithm. ©2016 SPIE and IS&T [DOI:10.1117/1.JEI.25.5.053017] [ABSTRACT FROM AUTHOR]

The dune field intersected by the Israel–Egypt borderline has attracted many remote sensing studies over the years because it exhibits unique optical phenomena in several domains, from the visual to the thermal infrared. These phenomena are the result of land-use policies implemented by the two countries, which have differing effects on the two ecosystems. This study explores the surface properties that affect radar backscatter, namely the surface roughness and dielectric properties, in order to determine the cause for the variation across the border. The backscatter contrast was demonstrated for SIR-C, the first synthetic aperture radar (SAR) sensor to capture this phenomenon, as well as ASAR imagery that coincides with complementary ground observations. These field observations along the border, together with an aerial image from the same year as the SIR-C acquisition were used to analyze differences in vegetation patterns that can affect the surface roughness. The dielectric permittivity of two kinds of topsoil (sand, biocrust) was measured in the field and in the laboratory. The results suggest that the vegetation structure and spatial distribution differ between the two sides of the border in a manner that is consistent with the radar observations. The dielectric permittivity of sand and biocrust was found to be similar, although they are not constant across the radar spectral region (50 MHz–20 GHz). These findings support the hypothesis that changes to the vegetation, as a consequence of the different land-use practices in Israel and Egypt, are the cause for the radar backscatter contrast across the border. [ABSTRACT FROM AUTHOR]

Autofocusing synthetic aperture imagery by maximizing a statistical quality metric such as contrast or sharpness is a well-documented approach in both synthetic aperture radar (SAR) literature and synthetic aperture sonar literature. It is most successfully applied in spotlight-mode SAR applications, where the assumption of spatial invariance of the corrupting phase function is strong and expressions for the gradients of various quality metrics with respect to standard error models have been calculated. Examples of application to stripmap imagery often involve sectioning images into small blocks, allowing spotlight algorithms to be patchwise applied. This paper formulates the gradient of the cost function in a manner that is consistent with the stripmap error model, inherently providing solutions that compensate for the spatial variance while simultaneously bypassing the need for subdividing an image or aperture. This paper formulates the stripmap gradient expression in conjunction with a computationally efficient imaging approach to rapidly achieve metric-maximizing solutions. Demonstrations are shown on a widebeam wideband rail-based system experiencing random jitter and on an unmanned-underwater-vehicle-mounted sonar system exhibiting artificially injected crabbing and sway errors. Results indicate that the algorithm is particularly effective at compensating for random and rapidly oscillating navigation and jitter errors, as well as sidelobes introduced by crabbing in arrayed systems. [ABSTRACT FROM PUBLISHER]

We propose the synthetic aperture wavefront sensing approach. It is based on acquiring several sets of measurements of the wavefront slopes by displacing sequentially the microlens array with respect to the unknown wavefront. These measurements are stacked together and processed as if obtained with a single-sampling array with an effective number of subpupils equal to the product of the number of microlenses by the number of displacements. We analyze and compare the performance of this approach with the method of modal coefficient averaging. The comparison is made in terms of the squared wavefront reconstruction error, spatially averaged over the pupil and statistically averaged over the noise and the aberrations of the population.We focused our attention on its applications to eye aberrometry. Our numerical results were obtained for a population statistics consistent with a wide sample of young adult eyes using different sampling grids and with several signal-to-noise ratios. They indicate that the synthetic aperture wavefront sensing is affected by less bias and noise propagation than the averaging method, providing smaller mean-squared estimation error. The number of complete Zernike radial orders that can be estimated using the synthetic aperture approach is consistently higher than that allowed by the conventional method [ABSTRACT FROM AUTHOR]

This paper addresses relativistic effects in bistatic and multistatic synthetic aperture radar (SAR) systems and missions. It is shown that the use of different reference frames for bistatic SAR processing and bistatic radar synchronization is prone to notable phase and time errors. These errors are a direct consequence of the relativity of simultaneity and can be explained in good approximation within the framework of Einstein's special theory of relativity. Using the invariance of the space-time interval, an analytic expression is derived that shows that the time and phase errors increase with increasing along-track distance between the satellites. The predicted errors are in excellent agreement with measurements from TanDEM-X and provide a satisfactory explanation for previously observed digital elevation map (DEM) height offsets that exceeded ±10 m. Consideration of the unexpected relativistic effects is essential for accurate DEM generation in TanDEM-X and has in the meantime been implemented in the operational processing chain. [ABSTRACT FROM AUTHOR]

Speckle noise is found in synthetic aperture radar (SAR) images and can affect visualization and analysis. A novel stochastic texture-based algorithm is proposed to suppress speckle noise while preserving the underlying structural and texture detail. Based on a sorted local texture model and a Fisher-Tippett logarithmic-space speckle distribution model, a Monte Carlo texture likelihood sampling strategy is proposed to estimate the true signal. The algorithm is compared to six other classic and state-of-the-art despeckling techniques. The comparison is performed both on synthetic noisy images added and on actual SAR images. Using peak signal-to-noise ratio, contrast-to-noise ratio, and structural similarity index as image quality metrics, the proposed algorithm shows strong despeckling performance when compared to existing despeckling algorithms. [ABSTRACT FROM AUTHOR]

The article discusses a study which documented the satellite history of coastal change in the eastern Amundsen Sea Embayment (ASE) since 1972 to understand the evolution of ice-shelf buttressing in the eastern ASE. Thirty-eight European Remote-sensing Satellite synthetic aperture radar scenes were compiled within the study area. The study concentrated on floating ice where surface slopes outside of crevasses are minimal, so discrepant orthorectification provides limited geodetic uncertainty. The terminus position through time along central flowlines of the eastern ASE's major outlet glaciers was identified from the imagery compilation.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission aims at producing global and frequent maps of SMOS and will be launched in 2008. SMOS' single payload is a new type of radiometer called Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) operating at L-band in which brightness temperature images are formed by a Fourier synthesis technique. However, in the alias-free field of view where the brightness temperature images are reconstructed, a bias is present which has been found to be higher for high- contrast brightness temperature scenes (coastlines) and lower for homogeneous scenes (all oceans or lands). This scene-dependent bias will ultimately limit the achievable accuracy of the retrieved geophysical parameters, and it is particularly critical for the retrieval of sea surface salinity. This paper presents a general analysis of the origin of this bias, which is found to be actually due to the different measurement errors in the instrument observables (visibility samples). An improvement of the image reconstruction algorithm is then presented to mitigate it. As compared with the previous algorithm versions, the proposed improved reconstruction algorithm further decomposes the visibility samples into some new terms: ocean and land/iced sea, instead of just the Earth's disk over the sky background. This decomposition aims at further reducing the contrast (high-frequency components) in the differential image and, therefore, minimizes the impact of multiplicative errors, improving the radiometric accuracy. In addition, this approach proves to perform the image reconstruction in part of the alias regions and improves the quality of the reconstruction close to the coastlines. [ABSTRACT FROM AUTHOR]

Interferometric radiometry is a novel concept in remote sensing that is also presenting particular challenges for calibration methods. In this paper, we describe the calibration subsystem (CAS) developed for the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) interferometer of the Soil Moisture and Ocean Salinity (SMOS) satellite. CAS is important for the overall performance of the payload as it calibrates out the differences between the multiple receivers of MIRAS. SMOS is in the final phase of development and is due to launch in 2008. [ABSTRACT FROM AUTHOR]

Initial results are presented for the new synthetic aperture radiometer, 2D-STAR, which is a dual-polarized L-band radiometer that employs aperture synthesis in two dimensions. This airborne instrument is the natural evolution of the Electronically Scanned Thinned Array Radiometer, which employs aperture synthesis only in the across-track dimension, and represents a further step in the development of aperture synthesis for remote sensing applications. 2D-STAR was successfully tested in June 2003 and, then, participated in the SMEX03 and SMEX04 soil moisture experiments. A description of the instrument and initial results in the form of first images and a preliminary comparison with changes in soil moisture during SMEX03 are presented here. [ABSTRACT FROM AUTHOR]

In this paper, we present a new similarity measure for automatic change detection in multitemporal synthetic aperture radar images. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are estimated by using a cumulant-based series expansion, which approximates probability density functions in the neighborhood of each pixel in the image. The degree of evolution of the local statistics is measured using the Kullback-Leibler divergence. An analytical expression for this detector is given, allowing a simple computation which depends on the four first statistical moments of the pixels inside the analysis window only. The proposed change indicator is compared to the classical mean ratio detector and also to other model-based approaches. Tests on the simulated and real data show that our detector outperforms all the others. The fast computation of the proposed detector allows a multiscale approach in the change detection for operational use. The so-called multiscale change profile (MCP) is introduced to yield change information on a wide range of scales and to better characterize the appropriate scale. Two simple yet useful examples of applications show that the MCP allows the design of change indicators, which provide better results than a monoscale analysis. [ABSTRACT FROM AUTHOR]

This article considers Synthetic Aperture Radar and other synthetic-aperture imaging systems in which a backscattered wave is measured from a variety of locations. We focus on the case in which the ground-reflectivity function depends on frequency as well as on position. We begin with a (linearized) mathematical model, based on a scalar approximation to Maxwell's equations, which includes the effects of the source waveform and the antenna beam pattern. The model can also accommodate other effects such as antenna steering and motion. For this mathematical model, we use the tools of microlocal analysis to develop and analyze a three-dimensional inversion algorithm that uses measurements made on a surface and determines the frequency-dependent ground reflectivity. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 28–34, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20004 [ABSTRACT FROM AUTHOR]