Large Synthetic Aperture Radar Images Research Articles

Density-based ship detection in SAR images: Extension to a self-similarity perspective

Nonlocal self-similarity is an important property of Synthetic Aperture Radar (SAR) images to characterize the repetitiveness of features embodied by SAR images within nonlocal areas and has been used for enhancement of SAR images. Existing SAR ship detectors often independently handle small sub-images cropped from a large marine SAR image and do not exploit the nonlocal self-similarity therein. In this paper, we propose a new ship detector from the perspective of nonlocal self-similarity in SAR images to improve the ship detection performance, basically including three stages: prescreening, intra-cue calculation, and inter-cue calculation. In the pre-screening stage, we design a new Histogram-based Density (HD) feature to rapidly select candidate sub-images potentially containing ship targets from a large SAR image. In the intra-cue calculation stage, target cues within a single candidate sub-image are extracted. In the inter-cue calculation stage, thanks to the nonlocal self-similarity among different candidate sub-images in terms of density features, we innovatively extract a weighted superpixel-HD map to obtain accumulated intra-cues across all the candidate sub-images. Finally, for each candidate sub-image, we fuse its inter-cue and intra-cue to obtain final detection results. Experimental results based on real SAR images show that our newly proposed method provides a better target-to-clutter contrast and ship detection performance than those of other state-of-the-art detection approaches.

SAR-to-Optical Image Translation Using Supervised Cycle-Consistent Adversarial Networks

Optical remote sensing (RS) data suffer from the limitation of bad weather and cloud contamination, whereas synthetic aperture radar (SAR) can work under all weather conditions and overcome this disadvantage of optical RS data. However, due to the imaging mechanism of SAR and the speckle noise, untrained people are difficult to recognize the land cover types visually from SAR images. Inspired by the excellent image-to-image translation performance of Generative Adversarial Networks (GANs), a supervised Cycle-Consistent Adversarial Network (S-CycleGAN) was proposed to generate large optical images from the SAR images. When the optical RS data are unavailable or partly unavailable, the generated optical images can be alternative data that aid in land cover visual recognition for untrained people. The main steps of SAR-to-optical image translation were as follows. First, the large SAR image was split to small patches. Then S-CycleGAN was used to translate the SAR patches to optical image patches. Finally, the optical image patches were stitched to generate the large optical image. A paired SAR-optical image dataset which covered 32 Chinese cities was published to evaluate the proposed method. The dataset was generated from Sentinel-1 (SEN-1) SAR images and Sentinel-2 (SEN-2) multi-spectral images. S-CycleGAN was applied to two experiments, which were SAR-to-optical image translation and cloud removal, and the results showed that S-CycleGAN could keep both the land cover and structure information well, and its performance was superior to some famous image-to-image translation models.

Double iterative optimal dictionary learning-based SAR image filtering method

The KSVD algorithm has been a focus of image filtering research ever since it was first developed. However, this algorithm has a slow convergence rate for sparse coding, and dictionary updating via singular value decomposition is too complex. Consequently, the filtering efficiency is very low for large synthetic aperture radar images, and a significant amount of edge texture information is lost. To address these issues, this paper proposes a double iterative optimal dictionary (DIOD) learning algorithm. First, during the sparse coding process, a single iteration is performed to select the optimal and second most optimal dictionary atoms for representing the residual error, and the selected atoms are then updated according to the accumulation of dictionary coefficients. Next, the normalized and weighted reconstruction error is used to update the dictionary. The experimental results indicate that the DIOD algorithm provides better edge preservation and has better operational efficiency than the KSVD algorithm while maintaining a high peak-signal-to-noise ratio. This approach effectively avoids the repetitive occurrence of dictionary atoms in the iteration process, accelerates the convergence rate, and increases the sparsity of the sparse coding coefficients while simplifying the dictionary updating method, enabling rapid filtering of large synthetic aperture radar images, and achieving an improved filtering effect.

An automatic method for mapping inland surface waterbodies with Radarsat-2 imagery

The use of synthetic aperture radar (SAR) imagery is generally considered to be an effective method for detecting surface water. Among various supervised/unsupervised classification methods, a SAR-intensity-based histogram thresholding method is widely used to distinguish waterbodies from land. A SAR texture-based automatic thresholding method is presented in this article. The use of texture images substantially enhances the contrast between water and land in intensity images. It also makes the method less sensitive to incidence angles than intensity-based methods. A modified Otsu thresholding algorithm is applied to selected sub-images to determine the optimal threshold value. The sub-images were selected using k-means results to ensure a sufficient number of pixels for both water and land classes. This is critical for the Otsu algorithm being able to detect an optimal threshold for a SAR image. The method is completely unsupervised and is suitable for large SAR image scenes. Tests of this method on a Radasat-2 image mosaicked from 8 QuadPol scenes covering the Spritiwood valley in Manitoba, Canada, show a substantial increase in land–water classification accuracy over the commonly used SAR intensity thresholding method (kappa indices are 0.89 vs. 0.79). The method is less computationally intensive and requires less user interaction. It is therefore well suited for detecting waterbodies and monitoring their dynamic changes from a large SAR image scene in a near-real time environment).

Accelerating thread-intensive and explicit memory management programs with dynamic partial reconfiguration

Recent research has shown that field programmable gate arrays (FPGAs) have a large potential for accelerating demanding applications, such as high performance digital signal process applications with low-volume market. The loss of generality in the architecture is one disadvantage of using FPGAs, however, the reconfigurability of FPGAs allow reprogramming for other applications. Therefore, a uniform FPGA-based architecture, an efficient programming model, and a simple mapping method are paramount for the wide acceptance of FPGA technology. This paper presents MASALA, a dynamically reconfigurable FPGA-based accelerator for parallel programs written in thread-intensive and explicit memory management (TEMM) programming models. Our system uses a TEMM programming model to parallelize demanding applications, including application decomposition into separate thread blocks and compute and data load/store decoupling. Hardware engines are included into MASALA using partial dynamic reconfiguration modules, each of which encapsulates a thread process engine that implements the hardware's thread functionality. A data dispatching scheme is also included in MASALA to enable the explicit communication of multiple memory hierarchies such as interhardware engines, host processors, and hardware engines. Finally, this paper illustrates a multi-FPGA prototype system of the presented architecture: MASALA-SX. A large synthetic aperture radar image formatting experiment shows that MASALA's architecture facilitates the construction of a TEMM program accelerator by providing greater performance and less power consumption than current CPU platforms, without sacrificing programmability, flexibility, and scalability.

Parallel Processing Strategies for Large SAR Image Data Sets in a Distributed Environment

Image processing of synthetic aperture radar (SAR) images is challenging due to distributed storage of input data sets and since appropriate algorithms are complex and time-consuming. Computers able to process these data in acceptable time usually are not at user's site. Our Concurrent and Distributed Image Processing (CDIP) system overcomes these problems and provides a platform-independent, transparent environment based on Java, CORBA and NetSolve. Users query a broker to find remote, high-performance servers on which the algorithms actually are executed. Key algorithms like image matching and Shape-from-Shading were parallelized mainly using MPI, and ported onto suitable computer architectures. Our experiments showed that all algorithms perform well, and they further proved the concept of CDIP to be beneficial: Usability of all integrated algorithms was significantly improved, mainly due to less user-centered network traffic, simple access to supercomputers, the creation of method sequences, and easy-to-use and well maintained algorithms.