SAR image processing using artificial intelligence planning

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

Although Synthetic Aperture Radar (SAR) can capture rich land cover information as a most important advanced technique in the field of international earth observation, the application effects still limited significantly. The reason is that the study of SAR imaging processing, SAR image processing and SAR applications are conducted respectively, the integrated study are lacked for parameter selection of SAR imaging processing and SAR image processing and targets identification of SAR application. Focusing on above science problems, the study for phase high fidelity model of high precision interferometric SAR, SAR non-stationary backscattering characteristics and cognition, SAR three-dimensional electromagnetic scattering modeling and its scattering mechanism and SAR multi-parameters optimization information extraction has been conducted. The typical natural distributed targets and man-made targets such as surface deformation, sea ice, building complex and collapsed building are selected, the method and application study oriented to SAR environmental parameters inversion are conduced, the main result and conclusion are as follows: (1) The overall theoretical frames of SAR information integrated processing was proposed; The simulation and ground validation system used to assess the effects of SAR information integrated processing was created; furthermore, the spaceborne SAR imaging algorithm with three steps focusing processing was proposed and realized the multi-modal integrated imaging processing and improved the imaging processing precision. (2) In terms of SAR information integrated processing of natural targets, focusing on phase high fidelity imaging processing of interferometric SAR surface deformation monitoring, an novel spaceborne SAR high fidelity simulation method based on stationary RCS and improved Goldstein SAR interferogram filter based on empirical mode decomposition were proposed. Focusing on multi-parameters optimization processing of sea ice types classification, the multi-channel spaceborne SAR sparse imaging method based on compressive sensing, Kalman filter for removal of scalloping and inter-scan banding in ScanSAR images and SVM sea ice classification method combined with sea ice concentration were proposed. (3) In terms of SAR information integrated processing of man-made targets, focusing on non-stationary backscattering from building complex, mitigation of azimuth ambiguities in spaceborne stripmap SAR iamges using selective restoration, SAR image despeckling by selective 3D filtering of multiple compressive reconstructed images and man-made target detection in urban areas based on a new azimuth stationary extraction method were proposed. Focusing on three-dimensional modeling and fast extraction of collapsed building, H-a-p method used for collapsed building extraction was proposed.

Synthetic aperture radar (SAR) imaging processing is a complex operation that requires a large amount of floating point computations. It is very time-consuming to convert SAR raw data into imagery on microcomputers. The authors have successively developed SAR processors using two kinds of computers-JN3AR of distributed memory multiprocessor architecture (made in China), and SGI Origin 200 of scalable shared memory multiprocessor architecture. The SAR imaging efficiency has been greatly improved.

Synthetic aperture radar (SAR) is able to form high-resolution images, which is beneficial for target identification and recognition. However, High-resolution SAR image formation is of immense computation, especially for large scene. In practice, the region of interest (ROI) that needs high resolution imaging may constitute only a small portion of the whole scene. Since ROI detection may only require image resolution much lower than the highest resolution that can be achieved from SAR data, it is efficient to generate an "adequately low resolution" image to find ROI. If target recognition is needed, full-resolution images can be generated for ROI. In this paper, a novel two-stage framework of SAR image formation and processing is developed. Fast factorized back-projection (FFBP) algorithm is introduced to generate SAR images recursively. The FFBP significantly reduces computation complexity with minimal loss in accuracy compared with traditional back-projection (BP) algorithm through recursive stages. At each stage the resolution of images is finer than previously. When the resolution is adequate for target detection, a detection procedure is processed to find ROI in the whole scene. Full resolution image for ROI is then generated by integrating the low resolution images. Moreover, since moving targets appear at different positions in the "adequately low-resolution" image series, moving targets can be also detected. Simulation results show that with single-pass high-resolution SAR data, the method generates low-resolution images quickly for ROI and moving target detection, and provides high-resolution details efficiently for ROI.

Synthetic aperture RADAR (SAR) is an emerging technology primarily used for high resolution ground mapping. SAR's capability to perform under adverse weather conditions, day or night makes it advantageous for many civilian and military applications. It is an active imaging system and inherently suffers from speckle noise, which is multiplicative in nature. Speckle noise originates from the interferen'ce of the coherent wave fronts associated with the reflected signal. The quality of SAR images is significantly degraded by this interference. Conventional intensity-based image processing techniques do not perform well for SAR images because of multiplicative nature of speckle. A variety of suggested image segmentation techniques suitable for SAR images have been analysed in this paper. The paper performs comparative analysis of image segmentation techniques that are based on different features of the images and are specialized to perform on SAR images. Experiments are performed on synthetically generated images and real SAR images obtained from Moving and Stationary Target Acquisition and Recognition (MSTAR) public database.

Normalized Difference Vegetation Index (NDVI) values extracted from remotely sensed optical imagery are used ubiquitously to monitor crop condition. However, challenges in the operational use of optical imagery are well documented making it difficult to capture measures of crop condition during critical phenology stages when clouds obscure. This study investigates the integration of Synthetic Aperture Radar (SAR) and optical imagery to characterize the condition of crop canopies in order to deliver daily measures of NDVI during the entire growing season. Multitemporal C-band polarimetric RADARSAT-2 SAR data and RapidEye images were acquired in 2012 for a study site in western Canada. SAR polarimetric parameters and NDVI were extracted. The temporal variations in SAR polarimetric parameters and NDVI were interpreted with respect to the development of the canola canopy. Optical NDVI was statistically related with SAR polarimetric parameters over test canola fields. Significant correlations were documented between a number of SAR polarimetric parameters and optical NDVI, in particular with respect to HV backscatter, span, volume scattering of the Freeman Durden decomposition and the radar vegetation index, with R-values of 0.83, 0.72, 0.81 and 0.71 respectively. Based on the statistical analysis, SAR polarimetric parameters were calibrated to optical NDVI, creating a SAR-calibrated NDVI (SARc-NDVI)). A canopy structure dynamics model (CSDM) was fitted to the SARc-NDVI, providing a seasonal temporal vegetation index curve. The coupling of NDVI from optical and SAR imagery with a CSDM demonstrates the potential to derive daily measures of crop condition over the entire growing season.

This paper proposes using a backpropagation (BP) neural network for the classification of ship targets in airborne synthetic aperture radar (SAR) imagery. The ship targets consisted of 2 destroyers, 2 cruisers, 2 aircraft carriers, a frigate and a supply ship. A SAR image simulator was employed to generate a training set, a validation set, and a test set for the BP classifier. The features required for classification were extracted from the SAR imagery using three different methods. The first method used a reduced resolution version of the whole SAR image as input to the BP classifier using simple averaging. The other two methods used the SAR image range profile either before or after a local-statistics noise filtering algorithm for speckle reduction. Performance on an extensive test set demonstrated the performance and computational advantages of applying the neural classification approach to targets in airborne SAR imagery. Improvements due to the use of multi-resolution features were also observed.

This article introduces the application of a physics-based symbolic image partitioning method to detect targets in synthetic aperture radar (SAR) imagery. ‘Targets’ in this case refer to vehicular objects which produce a distinct radar return pattern, and have spatial characteristics that are known a priori. The proposed Rotationally Invariant Symbolic Histogram (RISH) detection method co-analyses both target and speckle statistics, and significantly reduces computational requirements by partitioning the data into a discrete number of state representations. RISH requires only one pass for robust detection, unlike other SAR detection methods which rely on difference metrics calculated using multiple passes. To improve performance in high-resolution data, RISH uses a weighted feature extraction algorithm to avoid the common requirement of processing each pixel of the image equally. The weighted structure extracts geometrically undefined and rotationally invariant target features. This article details the analysis of 24 experimentally obtained very high-frequency (VHF)-band SAR magnitude images using this novel approach to SAR target detection. In localizing small (~8.4 m2) foliage-concealed targets, without the aid of pre-processing, this method results in high performance characteristics (90% true positive) with a low Type-II error rate of 6.4 false alarms per 1 × 106 m2. With the addition of change detection, RISH lowers the error rate by 85%.

<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</tex> -band synthetic aperture radar (SAR) imagery of the Goto Islands of Japan was digitally analyzed to extract air-sea interaction parameters and to assess the potential of texture measures in analysis of SAR ocean imagery. Wind direction is extracted from wind rows, wind streaks, and random turbulence patterns observed in the SAR imagery. Sea-state parameters are either extracted directly from the imagery or estimated using the extracted information in previously established empirical formulas. A convenient method of digitally presenting imagery, local power spectra, and the extracted/estimated parameters is presented. Texture analysis based on gray-level co-occurrence (GLC) matrices is applied to SAR ocean imagery. The inertia measure is shown to extract similar information to the power spectrum. The cluster-shade measure is shown to be sensitive to image phase.

The relationship between synthetic aperture radar (SAR) imagery of ocean waves and the ocean surface structure is discussed. The effects of ocean surface motion on SAR images is given. A generalization of conventional SAR correlation techniques is developed to provide imagery with the minimum possible degradation and distortion. The modeling of such imagery is discussed and an analytically tractable example given. Ongoing work towards the determination of the relevant components of surface microstructure is described. The future use of SAR phase information to supplement information obtainable from SAR imagery is suggested.

We study the problem of target identification from Synthetic Aperture Radar (SAR) imagery. Target classification using SAR imagery is a challenging problem due to large variations of target signature as the target aspect angle changes. Previous work on modeling wide angle SAR imagery has shown that point features, extracted from scattering center locations, result in a high dimensional feature vector that lies on a low dimensional manifold. In this paper we use rich probabilistic models for these target manifolds to analyze classification performance as a function of Signal-to-noise ratio (SNR) and Bandwidth. We employ Mixture of Factor Analyzers (MoFA) models to approximate the target manifold locally, and use error bounds for the estimation and analysis of classification error performance. We compare our performance predictions with the empirical performance of practical classifiers using simulated wideband SAR signatures of civilian vehicles.

Deep Neural Networks (DNNs) based Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems have shown to be highly vulnerable to adversarial perturbations that are deliberately designed yet almost imperceptible but can bias DNN inference when added to targeted objects. This leads to serious safety concerns when applying DNNs to high-stakes SAR ATR applications. Therefore, enhancing the adversarial robustness of DNNs is essential for applying DNNs to modern real-world SAR ATR systems. Toward building more robust DNN-based SAR ATR models, this article explores the domain knowledge of SAR imaging process and proposes a novel Scattering Model Guided Adversarial Attack (SMGAA) algorithm which can generate adversarial perturbations in the form of electromagnetic scattering response (called adversarial scatterers). The proposed SMGAA consists of two parts: 1) a parametric scattering model and corresponding imaging method and 2) a customized gradient-based optimization algorithm. First, we introduce the effective Attributed Scattering Center Model (ASCM) and a general imaging method to describe the scattering behavior of typical geometric structures in the SAR imaging process. By further devising several strategies to take the domain knowledge of SAR target images into account and relax the greedy search procedure, the proposed method does not need to be prudentially finetuned, and can efficiently find the effective ASCM parameters to fool the SAR classifiers and facilitate the robust model training. Comprehensive evaluations on the MSTAR dataset show that the adversarial scatterers generated by SMGAA are more robust to perturbations and transformations in the SAR processing chain than the currently studied attacks, and are effective to construct a defensive model against the malicious scatterers.

Synthetic aperture radar(SAR) imaging processing is a complex operation that requires large amounts of floating point computations. It is very time-consuming to convert SAR raw data into an image on microcomputers. This paper presents a digital imaging matrix algorithm. This algorithm includes range compression, azimuth compression and range walk correction which are implemented by matrix (vector) operation or matrix (vector) translation. It is available for implementation of SAR real-time imaging, and especially suitable for parallel computation on parallel processor, on vector processor. Especially, it provides a new design method for hardware process. The authors design a SQDSPS (SAR Quick-look Digital Signal Processing System) with the algorithm, which is different from the quick-look algorithm SPECAN. The experimental results indicate that the matrix algorithm is significant for saving the SAR imaging time.

It is important to determine quickly the extent of flooding during extreme cases. Even though SAR imagery with its own energy sources is highly applicable to flood monitoring owing to its sensitivity to the water area, topographic effects caused by local terrain relief must be carefully considered before the actual classification process. Since backscattering coefficients of the shadow area in high relief regions are very similar to those of the water area, it is essential to regard these areas before and after the classification procedure, although the process is a difficult and time-consuming task. In this study, efficient and economical methods for water area classification during floods in mountainous area are described. We tested five different cases using various synthetic aperture radar (SAR) image processing techniques, texture measures, and terrain shape information such as elevation and slope. The case whereby the SAR image was classified with the local slope information exhibited the best result for water area classification, even in small streams of different elevation categories. Consequently in mountainous areas, the combination of a SAR image and local slope information was the most appropriate method in estimating flooded areas.

The SEASAT satellite system, using an onboard 23-cm-wavelength synthetic aperture radar (SAR), collected approximately 25- to 40-m-resolution radar images of the ocean in 100-km-wide swaths ranging in length from about 300 to 3000 km. Here we report results from the 18 SEASAT SAR passes during the Joint Air-Sea Interaction (JASIN) experiment conducted off the west coast of Scotland in the summer of 1978. These many SAR images, when coupled with the intensive ship, buoy, and aircraft measurements of the JASIN experiment, provide a unique opportunity to assess the ability of satellite SAR to measure ocean surface phenomena, particularly surface wave fields. In this study we use only optically processed SAR images. Although gravity waves of length approximately 80 to 300 m are often seen in SAR imagery, they are not always seen. We find that SAR resolution and wave height are important criteria for determining wave visibility and suggest that wind velocity is also. Comparisons between SAR and buoy estimates of dominant wavelength and direction (including data from several other experiments) agree to within about ±14% and ± 10°, respectively. We use a focus sharpness algorithm to resolve the 180° directional ambiguity of SAR image directional estimates. Correlation of buoy measurements of significant wave height (H 1/3) with peak signal- to-noise ratio in Fourier transforms of SAR images (r = 0.7) suggests that H 1/3 can be estimated to an accuracy of about ± 1 m. However, a similar, but weaker, correlation exists between signal-to-noise and wave length, i.e., H 1/3 and wavelength are correlated in the JASIN data set. SAR image power spectra are in rough agreement with buoy measurements of omnidirectional ocean wave height spectra Ψ(K) and correspond less closely with ocean wave slope spectra Ψ(K) However, the dominant wavenumber (Kpeak.) in SAR spectra typically falls below the dominant wavenumber of corresponding buoy spectra. Further, the slope of SAR spectra for K K Peak is typically less steep than corresponding buoy spectra Ψ(K). These differences between SAR spectra and buoy measurements of form qualitative support for the imaging mechanisms developed by Alpers, Ross, and Rufenach. Analysis algorithms based on this theory, but including empirical modifications, should substantially improve estimates of Ψ (K,θ) using SAR images.