Optical Synthetic Aperture Research Articles

200mm Optical synthetic aperture imaging over 120 meters distance via Macroscopic Fourier ptychography

200mm Optical synthetic aperture imaging over 120 meters distance via Macroscopic Fourier ptychography

EFCformer: High-resolution image restoration network for optical synthetic aperture imaging system

EFCformer: High-resolution image restoration network for optical synthetic aperture imaging system

Stereoscopic depth perception through foliage

Both humans and computational methods struggle to discriminate the depths of objects hidden beneath foliage. However, such discrimination becomes feasible when we combine computational optical synthetic aperture sensing with the human ability to fuse stereoscopic images. For object identification tasks, as required in search and rescue, wildlife observation, surveillance, and early wildfire detection, depth assists in differentiating true from false findings, such as people, animals, or vehicles vs. sun-heated patches at the ground level or in the tree crowns, or ground fires vs. tree trunks. We used video captured by a drone above dense woodland to test users’ ability to discriminate depth. We found that this is impossible when viewing monoscopic video and relying on motion parallax. The same was true with stereoscopic video because of the occlusions caused by foliage. However, when synthetic aperture sensing was used to reduce occlusions and disparity-scaled stereoscopic video was presented, whereas computational (stereoscopic matching) methods were unsuccessful, human observers successfully discriminated depth. This shows the potential of systems which exploit the synergy between computational methods and human vision to perform tasks that neither can perform alone.

Mid-frequency MTF compensation for optical synthetic aperture based on baseline transform scanning via deep learning

Optical synthetic aperture (OSA) structure of multiple in-phase circular sub-mirrors greatly improves the spatial resolution of space telescopes, but the sparsity and discretization lead to a degradation of mid-frequency response of modulation transfer function (MTF) and loss of image information. A method for improving mid-frequency MTF is presented, named deep learning baseline transform scanning (DLBTS). By performing system baseline transformations, a series of low-resolution imaging sequences containing the missing mid-frequency modulation transfer functions are obtained. Leveraging the powerful feature extraction capability of deep learning, different resolution image sequences are fused to compensate for the missing mid-frequency information. Reverse Regression Module (RRM) is designed to guarantee the loss optimality. When SNR = 30 dB, the PSNR of the Golay-3 image with single-system compensation can be improved from 22.84 dB to 26.29 dB, SSIM can be from 0.685 to 0.765, and MS-SSIM can be raised from 0.865 to 0.916.

Experimental demonstration of scene-based cophasing in optical synthetic aperture imaging using the SPGD algorithm

Large-aperture telescopes based on optical synthetic aperture imaging are investigated for recent high-resolution spaceborne observations. An enabling technique of aperture synthesis is a cophasing method to suppress a piston-tip-tilt error between sub-apertures. This paper proposes a scene-based cophasing technique using the stochastic parallel gradient descent (SPGD) algorithm, assuming application to high-resolution Earth observation. A significant advantage of the SPGD algorithm is a model-less cophasing capability based on extended scenes, but the simultaneous scene-based piston-tip-tilt correction between multiple apertures has not been demonstrated. In this paper, we developed a tabletop synthetic aperture imaging system with 37 sub-apertures and demonstrated extended-scene-based piston-tip-tilt control by optimizing applied voltages to 111 actuators simultaneously. The demonstration experiments used not only static scenes but also a time-varying dynamic scene for observation targets. In every measurement, the proposed scene-based approach reduced the initially defined piston-tip-tilt errors, and the image sharpness significantly improved, although the correction rate in the dynamic scene observation was slower. Finally, this paper discusses the influence of scene dynamics on image-based cophasing.

Super-resolution reconstruction method of the optical synthetic aperture image using generative adversarial network

Abstract In order to solve the contradiction between large aperture elements and high-resolution images, in this study, we propose an improved image-resolution method based on generative adversarial network (GAN). First, we analyze the imaging principle of the optical synthetic aperture. Further, we improve a super-resolution GAN; especially, this network uses a multi-scale convolutional cascade to obtain global features of the image, and a multi-scale receptive field block and residual in residual dense block are built to obtain image details. In addition, this study uses the Mish function as the activation function of the discriminator to solve the problems of neuron extreme, gradient explosion, and poor generalization ability of the model. Through simulation, the results show that the proposed method can achieve a peak signal-to-noise ratio (PSNR) of 30 dB compared with traditional image super-resolution reconstruction methods for synthetic aperture image. The method proposed has an improvement of 2 dB in the PSNR and 0.016 in structure similarity index measure compared with the original super-resolution GAN. Therefore, this method can effectively reduce the image distortion and improve the quality of image reconstruction.

Space-based Distributed Optical Synthetic Aperture Techniques

Space-based Distributed Optical Synthetic Aperture Techniques

Research on Co‐Phasing Closed‐Loop Experiment for Optical Synthetic Aperture Using Deep Learning

Research on Co‐Phasing Closed‐Loop Experiment for Optical Synthetic Aperture Using Deep Learning

OPA-LiDAR active optical synthetic aperture super-resolution detection imaging technology

OPA-LiDAR active optical synthetic aperture super-resolution detection imaging technology

RPIR: A Semiblind Unsupervised Learning Image Restoration Method for Optical Synthetic Aperture Imaging Systems With Co-Phase Errors

RPIR: A Semiblind Unsupervised Learning Image Restoration Method for Optical Synthetic Aperture Imaging Systems With Co-Phase Errors

Distributed optical synthetic aperture position-transformed method with high-frequency domain coverage.

Realizing an ultrahigh-equivalent aperture for space-based direct optical detection using a distributed optical synthetic aperture (DOSA) system with a very low filling ratio is challenging. This study proposes a position-transformation method for DOSA systems with high-frequency domain coverage called High-Frequency domain-Covering discrete Archimedean Spiral Arrays (HFCASA). The study shows that Golay3 HFCASA with a filling ratio of 0.0675% can greatly improve frequency domain coverage and fulfill the resolution requirements of a 200 m aperture telescope. HFCASA provides the theoretical basis for the future deep-space exploration of DOSA.

Image restoration for optical synthetic aperture system via variational physics-informed network

Optical synthetic aperture with homogeneous circular sub-mirrors greatly improves the spatial resolution of space telescopes; however, the discrete and sparse characteristics of the sub-mirrors reduce the mid-frequency modulation transfer function (MTF), resulting in blurred images being obtained. In this paper, a method combining variational physics-informed with deep learning is presented, which shows blind image restoration without complex priors. The constraint effect of traditional maximum a posterior (MAP) framework is removed by variational inference framework, which is embedded into Variational Physics-informed Network (VPIN) to optimize neural network training. Residual dense blocks (RDBs) construction is contributed to image feature extraction. Networks with SSIM-corrected loss functions can be trained at the feature level to help with convergence. When SNR = 30 dB, the PSNR of Golay-6 remote sensing test set increases from 20.16 dB to 23.90 dB, SSIM is from 0.610 to 0.842, and MS-SSIM is from 0.930 to 0.955.

A Remote Sensing View of the 2020 Extreme Lake-Expansion Flood Event into the Peace–Athabasca Delta Floodplain—Implications for the Future SWOT Mission

The Peace–Athabasca Delta (PAD) in western Canada is one of the largest inland deltas in the world. Flooding caused by the expansion of lakes beyond normal shorelines occurred during the summer of 2020 and provided a unique opportunity to evaluate the capabilities of remote sensing platforms to map surface water expansion into vegetated landscape with complex surface connectivity. Firstly, multi-source remotely sensed data via satellites were used to create a temporal reconstruction of the event spanning May to September. Optical synthetic aperture radar (SAR) and altimeter data were used to reconstruct surface water area and elevation as seen from space. Lastly, temporal water surface area and level data obtained from the existing satellites and hydrometric stations were used as input data in the CNES Large-Scale SWOT Simulator, which provided an overview of the newly launched SWOT satellite ability to monitor such flood events. The results show a 25% smaller water surface area for optical instruments compared to SAR. Simulations show that SWOT would have greatly increased the spatio-temporal understanding of the flood dynamics with complete PAD coverage three to four times per month. Overall, seasonal vegetation growth was a major obstacle for water surface area retrieval, especially for optical sensors.

Wide-spectrum optical synthetic aperture imaging via spatial intensity interferometry

High resolution imaging is achieved using increasingly larger apertures and successively shorter wavelengths. Optical aperture synthesis is an important high-resolution imaging technology used in astronomy. Conventional long baseline amplitude interferometry is susceptible to uncontrollable phase fluctuations, and the technical difficulty increases rapidly as the wavelength decreases. The intensity interferometry inspired by HBT experiment is essentially insensitive to phase fluctuations, but suffers from a narrow spectral bandwidth which results in a lack of effective photons. In this study, we propose optical synthetic aperture imaging based on spatial intensity interferometry. This not only realizes diffraction-limited optical aperture synthesis in a single shot, but also enables imaging with a wide spectral bandwidth, which greatly improves the optical energy efficiency of intensity interferometry. And this method is insensitive to the optical path difference between the sub-apertures. Simulations and experiments present optical aperture synthesis diffraction-limited imaging through spatial intensity interferometry in a 100 nm spectral width of visible light, whose maximum optical path difference between the sub-apertures reaches 69<italic>λ</italic>. This technique is expected to provide a solution for optical aperture synthesis over kilometer-long baselines at optical wavelengths.

Image restoration for optical synthetic aperture system via patched maximum–minimum intensity prior and unsupervised DenoiseNet

Optical synthetic aperture system improves spatial resolution significantly. However, the image will be degraded and blurred because of the sparse sub-mirror arrangement. Therefore, it is of great significance to exploit the restoration method to obtain an ideal latent image. A method combining maximum a posterior (MAP) framework model optimization with deep learning is presented to enhance imaging quality. The model is optimized by patched maximum–minimum intensity (PMMI) to alleviate insufficient representation of existing priors, while DenoiseNet is designed to train the device and eliminate noise interference. Unsupervised learning is applied to solve the acquisition difficulty of original image labels dispensed with clean samples as training set. When a noise of 30 dB is added, the PSNR of the remote sensing test set increases from 20.90 dB to 23.01 dB. SSIM ameliorates from 0.345 to 0.651 by 88.6% and MS-SSIM from 0.781 to 0.883 by 13.1%. The mid-frequency MTF also has a significant improvement.

High-Accuracy Online Calibration Scheme for Large-Scale Integrated Photonic Interferometric Measurements

High-accuracy interferometric measurement is crucial for integrated photonic interferometric imaging. In this paper, we propose an online calibration approach for large-scale integrated photonic beam combination that can realize high-accuracy measurement. By leveraging a simple four-step switching condition and the optical path differences between interferometric beams, the instrumental characteristics of the beam combiner, including the transmittances, visibilities, and phase relations, could be calibrated accurately. Compared to the conventional approach, the calibration procedure and estimation method are greatly simplified in our proposed scheme. The effectiveness of the proposed approach is validated using a photonic integrated circuit with two interferometric channels. The calibration results show that the differences between the phase relations are less than 1.41% between the proposed and conventional approaches. The proposed scheme shows significant advantages in optical synthetic aperture imaging systems using photonic integrated circuits especially for applications in harsh environments.

Assimilation of leaf Area Index from multisource earth observation data into the WOFOST model for sugarcane yield estimation

ABSTRACT Crop Growth Models (CGM) have been widely used in estimating crop yield at a local scale while Remote Sensing (RS) data has the advantage of retrieving crop parameters such as leaf Area Index (LAI) at a range of spatial scales. Data Assimilation (DA) is highly useful tool that integrates CGM and RS data derived from satellite imageries to improve the simulated crop state variables and consequently model outputs such as crop total biomass and yield. In this study, we assimilated LAI with the WOrld FOod STudies (WOFOST) model to estimate sugarcane yield using an Ensemble Kalman Filter (EnKF) algorithm. The LAI was retrieved from Landsat 8 (L8) optical and Sentinel 1A (S1A) Synthetic Aperture Radar (SAR) imageries using a Gaussian Process regression (GPR) method. The Deterministic Modeling (DM), independent assimilations of LAI retrieved from L8 and S1A, and assimilation of LAI retrieved from a combined SAR-optical data were tested and validated using field observation data in the Wonji-Shoa sugar plantation, Ethiopia. The results demonstrate that the accuracy of sugarcane yield estimated by the WOFOST model was significantly improved after DA using combined L8 and S1A data. Compared to the DM estimation, the root mean square error (RMSE) was decreased by 2.13 t/ha for the independent assimilations of LAI retrieved from L8, 3.96 t/ha for the independent assimilations of LAI retrieved from S1A and 5.94 t/ha for combined assimilation of L8 and S1A LAI. A coefficients of determination (R2) of 0.36, 0.48, 0.53, and 0.69 and Normalized Root mean square error (NRMSE) of 14.72%, 11.67%, 10.55%, and 8.44% were obtained for DM, L8 LAI assimilation alone, S1A LAI assimilation alone and combined L8 and S1A LAI assimilation, respectively. The results show that combined L8 and S1A LAI DA has better performance because SAR and optical data have complementary effects. Hence, the assimilation of LAI from combined L8 and S1A data into the WOFOST model provides a robust technique to improve crop yield estimations.

Imaging Effect of Vibration on Optical Synthetic Aperture

Imaging Effect of Vibration on Optical Synthetic Aperture

RestoreNet-Plus: Image restoration via deep learning in optical synthetic aperture imaging system

The synthetic aperture technology can improve the resolution effectively in the optical imaging system. In fact, the imaging blur, turbulence aberration and noise can affect the imaging quality of optical synthetic aperture imaging system seriously. Several non-blind methods are applied generally to recover the degraded maps with the prior information. However, the restoration effect is not stable enough and satisfactory. As a data-driven approach, the deep learning framework possesses advantages in solving this problem. In this paper we propose an improved network, RestoreNet-Plus, for the image restoration of optical synthetic aperture imaging system. After the proofs of numerical simulation and experiment results, RestoreNet-Plus is a better alternative compared with other methods, owing to its better restoration ability, strong denoising ability and capacity for turbulence correction error.

DETECTION OF OIL POLLUTION IMPACTS ON VEGETATION USING SAR AND MULTISPECTRAL IMAGES

Oil pollution damages terrestrial ecosystems. Now there is a need to improve existing methods for detecting, mapping and quantifying existing oil spills at an early stage. The paper discusses the method of oil spill detection based on the spectral characteristics of an optical image and synthetic aperture radar (SAR) data.