Low-resolution Synthetic Aperture Radar Images Research Articles

Towards Lightweight Deep Classification for Low-Resolution Synthetic Aperture Radar (SAR) Images: An Empirical Study

Numerous works have explored deep models for the classification of high-resolution natural images. However, limited investigation has been made into a deep classification for low-resolution synthetic aperture radar (SAR) images, which is a challenging yet important task in the field of remote sensing. Existing work adopted ROC–VGG, which has a huge amount of parameters, thus limiting its application in practical deployment. It remains unclear whether the techniques developed in high-resolution natural images to make the model lightweight can be effective for low-resolution SAR images. Therefore, with prior work as the baseline, this work conducts an empirical study, testing three popular lightweight techniques: (1) channel attention module; (2) spatial attention module; (3) multi-stream head. Our empirical results show that these lightweight techniques in the high-resolution natural image domain can also be effective in the low-resolution SAR domain. We reduce the parameters from 9.2M to 0.17M while improving the performance from 94.8% to 96.8%.

Optical-guided residual learning network for synthetic aperture radar image super-resolution

Synthetic aperture radar (SAR) image super-resolution (SR) has broad application prospects in military reconnaissance, marine monitoring, crop monitoring, and other fields. However, when the scale factor is large, current SAR image SR has limitations, such as insufficient feature extraction, difficulty of feature representation, and modeling difficulty of SR reconstruction. These problems have severely restricted the SR performance of SAR images. In addition, optical remote sensing images share higher resolution and richer high-frequency information than SAR remote sensing images. Therefore, we adopt high-resolution (HR) optical images as auxiliary images of SAR image SR to improve the performance of SAR image SR. Our research is conducted from two aspects: the extraction efficiency of HR optical image-guided information and the representation ability of low-resolution (LR) SAR image features. We propose an optical guidance residual networks (OGRNs) based on residual learning for SAR image SR. The OGRN uses optical images during network training, which enhances the performance of SAR image SR, even when optical images are missing during network testing. First, we extract the texture feature of SAR images through the designed residual learning module. Second, we extract the weight map of feature information for SAR image SR from HR optical images by the convolutional layer. Finally, the weight map is used to guide the weighted supervised SR reconstruction for SAR images. Extensive experimental results demonstrated that, under the guidance of optical images, OGRN achieves excellent performance in terms of both quantitative assessment metrics and visual quality.

Airborne SAR Radiometric Calibration Based on Improved Sliding Window Integral Method.

To verify the performance of the high-resolution fully polarimetric synthetic aperture radar (SAR) sensor carried by the Xinzhou 60 remote-sensing aircraft, we used corner reflectors to calibrate the acquired data. The target mechanism in high-resolution SAR images is more complex than it is in low-resolution SAR images, the impact of the point target pointing error on the calibration results is more obvious, and the target echo signal of high-resolution images is more easily affected by speckle noise; thus, more accurate extraction of the point target position and the response energy is required. To solve this problem, this paper introduces image context information and proposes a method to precisely determine the integration region of the corner reflector using sliding windows based on the integral method. The validation indicates that the fully polarimetric SAR sensor on the Xinzhou 60 remote-sensing aircraft can accurately reflect the radiometric characteristics of the ground features and that the integral method can obtain more stable results than the peak method. The sliding window allows the position of the point target to be determined more accurately, and the response energy extracted from the image via the integral method is closer to the theoretical value, which means that the high-resolution SAR system can achieve a higher radiometric calibration accuracy. Additionally, cross-validation reveals that the airborne SAR images have similar quality levels to Sentinel-1A and Gaofen-3 images.

Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method.

Nonparametric blind SAR image super resolution based on combination of the compressive sensing and sparse priors

This paper by proposing a novel approach, is one the first works that addresses the highly ill-posed problem of nonparametric blind single image super resolution (SISR) of the synthetic aperture radar (SAR) images. Combination of an adaptive compressive sensing (CS) technique and some effective sparse priors, as a powerful regularizer in the both high resolution (HR) image reconstruction and the point spread function (PSF) estimation domains is the fundamental idea of the proposed method. This task is formulated as a new cost function to be minimized with respect to an intermediate reconstructed HR image patch and a nonparametric PSF kernel, according to the alternative minimization (AM) algorithm. To solve the optimization of cost function, a numerical scheme based on the conjugate gradient least squares (CGLS) method is proposed. Experimental results for the both synthetic and realistic low resolution (LR) SAR images demonstrate that the proposed method achieves the state-of-the-art performance.

Registration of multitemporal low-resolution synthetic aperture radar images based on a new similarity measure

Image registration is concerned with the precise overlap of two images. One challenging problem in this area is the registration of low-resolution synthetic aperture radar (SAR) images. In general, extracting feature points from such images is difficult due to the coarse observation and the severe speckle. The use of area similarity for image registration is another important branch to solve the problem. A similarity measure based on a conditional density function (cdf) is proposed. The cdf is specially tailored for SAR images, where the speckle is generally assumed as multiplicative gamma noise with unit mean. Additionally, a two-step procedure is devised for the registration of intro-model SAR images to improve the computational efficiency. First, the two images are roughly aligned considering only the translational difference. Then small blocks from the two images are accurately aligned and the center point of each block is treated as a control point, which is finally used to obtain the precise affine transformation between the two images. Five SAR image datasets are tested in the experiment part, and the results demonstrate the efficiency and accuracy of the proposed method.

SAR Image Categorization With Log Cumulants of the Fractional Fourier Transform Coefficients

With the advent of high-resolution (HR) synthetic aperture radar (SAR) images from satellites like TerraSAR-X and TanDEM-X, interest is now on patch-oriented image categorization in contrast to the pixel-based classification in low-resolution SAR images. SAR image categorization requires the generation of a compact feature descriptor that accurately defines the content of the image patch under consideration. As phase information plays a critical role in SAR images, this paper proposes the use of a chirplet-derived transform, i.e., the fractional Fourier transform (FrFT), for generating a compact feature descriptor for single-look complex (SLC) SAR images. Representing a SAR signal in rotated joint time-frequency planes via the FrFT allows discovering the underlying backscattering phenomenon of the objects on the ground. SAR image projections on different planes of the joint time-frequency space using the FrFT provide a simple statistical response that is easier to analyze. The proposed method has been compared with a multiscale approach, i.e., Gabor filter banks, a second-order-statistics-based method (as gray-level co-occurrence matrices), and a spectral descriptor method. We demonstrate the suitability of the FrFT-based method for image categorization on the basis of backscattering behavior, whereas the Gabor-filter-bank-based method is found mainly suitable for images with a strong texture. This paper demonstrates enhancement in the separability for most of the considered categories when using logarithmic cumulants instead of linear moments for both the FrFT-based and Gabor-filter-bank-based methods. The experimental database consists of 2000 image patches (of size 200 × 200 pixels) extracted from SLC HR TerraSAR-X scenes.