Search

Synthetic aperture radar (SAR) is an active microwave imaging sensor for high-resolution observation, with the ability of working in all-weather and all-day. Recently, SAR images have been widely used in many fields. Among them, ship detection in single-channel SAR images is a significant part of civilian and military fields. This article first discusses the characteristic of SAR images and the detectability of ships, then summarizes the recent advance of traditional and deep learning-based methods used for ship detection in single-channel SAR images. In addition, the characteristics and existing problems of various methods are discussed and their future development trends are predicted. Aiming at the problems of the large amount of calculation, multi-scale and densely docked ship detection in single-channel SAR images, an improved deep learning-based detection algorithm is proposed, which has achieved excellent performance on the SAR ship detection dataset (SSDD). [ABSTRACT FROM AUTHOR]

Ship detection in low-quality Synthetic Aperture Radar (SAR) images poses a persistent challenge. Noise signals in complex environments disrupt imaging conditions, hindering SAR systems from acquiring precise target information, thereby significantly compromising the performance of detectors. Some methods mitigate interference via denoising techniques, while others introduce noise using classical methods to learn target features in the presence of noise. This conundrum is regarded as a cross-domain problem in this paper; a ship detection method in low-quality images is proposed to learn features of targets and shrink serious deterioration of detection performance by utilizing Generative Adversarial Networks (GANs). First, an image-to-image translation task is implemented using CycleGAN to generate low-quality SAR images with complex interference from the source domain to the target domain. Second, with the annotation inheritance, these generated SAR images participate in a training process to improve the detection accuracy and model robustness. Multiple experiments indicate that the proposed method conspicuously improves the detection performance and efficaciously reduces the missed detection rate in the SAR ship detection task. This cross-domain approach achieved outstanding improvements in the form of 11.0% mAP and 3.22% mAP on the GaoFen-3 ship dataset and SRSSD-V1.0, respectively. In addition, the characteristics and potentials of near-shore and off-shore SAR image reconstruction with style transfer based on Generative Adversarial Networks were explored and analyzed in this work. [ABSTRACT FROM AUTHOR]

Due to the unique imaging mechanism of synthetic aperture radar (SAR), targets in SAR images often shows complex scattering characteristics, including unclear contours, incomplete scattering spots, attitude sensitivity, etc. Automatic aircraft detection is still a great challenge in SAR images. To cope with these problems, a novel approach called adaptive deformable network (ADN) combined with peak feature fusion (PFF) is proposed for aircraft detection. The PFF is designed for taking full advantage of the strong scattering features of aircraft, which consists of peak feature extraction and fusion. To fully exploit the strong scattering features of the aircraft in SAR images, peak features are extracted via the Harris detector and the eight-domain pixel detection of local maxima. Then, the saliency of aircraft under multiple imaging conditions is enhanced by multi-channel blending. All the PFF-preprocessed images are fed into the ADN for training and testing. The core components of ADN contain an adaptive spatial feature fusion (ASFF) module and a deformable convolution module (DCM). ASFF is utilized to reconcile the inconsistency across different feature scales, raising the characterization capabilities of the feature pyramid and improving the detection performance of multi-scale aircraft further. DCM is introduced to determine the 2-D offsets of feature maps adaptively, improving the geometric modeling abilities of aircraft in various shapes. The well-designed ADN is established by combining the two modules to alleviate the problems of the multi-scale targets and attitude sensitivity. Extensive experiments are conducted on the GaoFen-3 (GF3) dataset to demonstrate the effectiveness of the PFF-ADN with an average precision of 89.34%, as well as an F1-score of 91.11%. Compared with other mainstream algorithms, the proposed approach achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for ship detection and discrimination in complex background from synthetic aperture radar (SAR) images. It first implements a pixel-level land–sea segmentation with the aid of a global 250-m water mask. Then, an efficient multiscale constant false alarm rate (CFAR) detector with generalized Gamma distribution clutter model is designed to detect candidate targets in the sea. At last, eigenellipse discrimination and maximum-likelihood (ML) discrimination are designed to further exclude false alarm nonship objects in nearshore and harbor area. The proposed land–sea segmentation method is compared with multilevel Otsu method. The proposed multiscale ship detector is compared with conventional CFAR detectors. These contrast experiments show the good performance of our method. Finally, experiments undertaken on actual ALOS-2 SAR data show the efficacy of the proposed approach in detecting nearshore ship targets in a complex coastal environment. [ABSTRACT FROM PUBLISHER]

Deep convolutional neural networks have achieved great success in computer vision and many other areas. They automatically extract translational-invariant spatial features and integrate with neural network-based classifier. This letter investigates the suitability and potential of deep convolutional neural network in supervised classification of polarimetric synthetic aperture radar (POLSAR) images. The multilooked POLSAR data in the format of coherency or covariance matrix is first converted into a normalized 6-D real feature vector. The six-channel real image is then fed into a four-layer convolutional neural network tailored for POLSAR classification. With two cascaded convolutional layers, the designed deep neural network can automatically learn hierarchical polarimetric spatial features from the data. Two experiments are presented using the AIRSAR data of San Francisco, CA, and Flevoland, The Netherlands. Classification result of the San Francisco case shows that slant built-up areas, which are conventionally mixed with vegetated area in polarimetric feature space, can now be successfully distinguished after taking into account spatial features. Quantitative analysis with respect to ground truth information available for the Flevoland test site shows that the proposed method achieves an accuracy of 92.46% in classifying the considered 15 classes. Such results are comparable with the state of the art. [ABSTRACT FROM PUBLISHER]

This article analyses the anisotropy of polarimetric scattering changing with azimuth incidence angle using a multi-look processed synthetic aperture radar (SAR) image. First, three canonical scattering models were developed to simulate the migration tracks on the Cameron polarimetric space. The migration tracks indicate that these polarimetric parameters have anisotropic property. Second, unmanned aerial vehicle synthetic aperture radar (UAVSAR) data are used to validate the simulated results. The Cameron scattering-type parameter z and the orientation angle calculated by SAR data are consistent with the simulated results by small perturbation method (SPM) double-scattering. Finally, based on the anisotropic analysis, a new method of extracting polarimetric information is proposed. Using this method, six parameters were obtained and two additional parameters, Purity and Stability, were derived. These parameters contain specific physical meaning and are useful in the recognition of the scattering mechanism. Purity can be used to recognize the simple structure scatterers with zero orientation. Stability has the potential to describe the dynamic property of scatterers. [ABSTRACT FROM AUTHOR]

The algorithm of synthetic aperture radar automatic target recognition (SAR-ATR) is generally composed of the extraction of a set of features that transform the raw input into a representation, followed by a trainable classifier. The feature extractor is often hand designed with domain knowledge and can significantly impact the classification accuracy. By automatically learning hierarchies of features from massive training data, deep convolutional networks (ConvNets) recently have obtained state-of-the-art results in many computer vision and speech recognition tasks. However, when ConvNets was directly applied to SAR-ATR, it yielded severe overfitting due to limited training images. To reduce the number of free parameters, we present a new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set illustrate that A-ConvNets can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants. [ABSTRACT FROM PUBLISHER]

In order to detect targets from nonuniform background in synthetic aperture radar (SAR) images, this paper proposes a new approach for saliency detection based on the idea of pattern recurrence. The pattern recurrence quantifies the saliency of a local patch by how well it can be reconstructed from patches in the background. The similarity between polarimetric SAR (PolSAR) patches is defined as the likelihood that both patches belong to the same Wishart distribution. A simple saliency indicator is defined as the normalized variance of the nonlocal similarity map. To detect target of different sizes, multiscale patches are also used. The proposed approach is first tested on optical images and results are compared with other saliency detection method and experiments of eye fixation prediction. Test results on real SAR images show that the proposed method has robust performance on target detection with and without the presence complicated background. [ABSTRACT FROM PUBLISHER]

Man-made building objects mostly with vertical wall structures may present distinct scattering patterns, e.g., wall/roof upfront scattering, wall-ground double scattering, etc., along the range dimension in high-resolution synthetic aperture radar (SAR) images. In this letter, a 1-D detector, referred to as the “range detector,” is presented for building detection, which operates only along the range direction. Experiments show that this range detector can effectively detect and extract the footprint of the illuminated wall of a cuboid building, with which the outline of the building image can be captured by marching the footprint toward radar. This approach is applied to an airborne Pi-SAR image of Sendai, Japan, and more than 80% of the buildings can be identified. The building height and length are also estimated, and the errors are found around 4–5 m based on optical image. [ABSTRACT FROM PUBLISHER]

As an all-weather and all-day remote sensing image data source, SAR (Synthetic Aperture Radar) images have been widely applied, and their registration accuracy has a direct impact on the downstream task effectiveness. The existing registration algorithms mainly focus on small sub-images, and there is a lack of available accurate matching methods for large-size images. This paper proposes a high-precision, rapid, large-size SAR image dense-matching method. The method mainly includes four steps: down-sampling image pre-registration, sub-image acquisition, dense matching, and the transformation solution. First, the ORB (Oriented FAST and Rotated BRIEF) operator and the GMS (Grid-based Motion Statistics) method are combined to perform rough matching in the semantically rich down-sampled image. In addition, according to the feature point pairs, a group of clustering centers and corresponding images are obtained. Subsequently, a deep learning method based on Transformers is used to register images under weak texture conditions. Finally, the global transformation relationship can be obtained through RANSAC (Random Sample Consensus). Compared with the SOTA algorithm, our method's correct matching point numbers are increased by more than 2.47 times, and the root mean squared error (RMSE) is reduced by more than 4.16%. The experimental results demonstrate that our proposed method is efficient and accurate, which provides a new idea for SAR image registration. [ABSTRACT FROM AUTHOR]

A simple method is described to estimate forest biomass by high-resolution polarimetric synthetic aperture radar (SAR). The method is based on the regression analysis between the measured biomass from the ground survey and the second intensity moment of the non-Gaussian texture in the cross-polarized L-band SAR images. The SAR data used in the analysis were acquired by the airborne polarimetric interferometric SAR over the coniferous forest in Hokkaido, Japan. The regression analysis was first carried out, and a model function was derived to relate the intensity moment and the measured biomass in 19 forest stands. Using this model function, the biomass values were estimated and compared with those of 21 different stands with known biomass. The average accuracy of the moment model was found to be 85%, which is similar to that of the previous K-distribution model. The advantage of this method over the distribution-based model is that there is no need to search a specific distribution function which fits best to the image texture. [ABSTRACT FROM PUBLISHER]

The algorithm of synthetic aperture radar (SAR) for automatic target recognition consists of two stages: feature extraction and classification. The quality of extracted features has significant impacts on the final classification performance. This paper presents a SAR automatic target classification method based on the wavelet-scattering convolution network. By introducing a deep scattering convolution network with complex wavelet filters over spatial and angular variables, robust feature representations can be extracted across various scales and angles without training data. Conventional dimension reduction and a support vector machine classifier are followed to complete the classification task. The proposed method is then tested on the moving and stationary target acquisition and recognition (MSTAR) benchmark data set and achieves an average accuracy of 97.63% on the classification of ten-class targets without data augmentation. [ABSTRACT FROM AUTHOR]