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A quadrilateral filtering algorithm is proposed in this letter to reduce noise in video synthetic aperture radar (video SAR). The novel filtering approach draws inspiration from the traditional bilateral filter and effectively exploits the similarities in both greyscale and geometric levels. Moreover, the proposed quadrilateral filter incorporates the similarity of time domain and rank-ordered absolute difference (ROAD) statistic for detecting the abundant speckle and impulse noise in video SAR frames. An impulse detector, which is related to the distribution of ROAD, is added in the filtering process to remove the outliers in the intensity domain after time redundancy processing with the target frame image. The proposed quadrilateral filter can obviously smooth various sources of noise, including strong speckle noise and impulse noise, while considering the details of each image frame of the video SAR. The new algorithm has been verified to achieve superior filtering performance with equivalent number of looks (ENL) values increased by 25% on average comparing with other widely used algorithms. [ABSTRACT FROM AUTHOR]

A hydrofoil is a basic shape of fluid machinery blades, and its drag reduction performance is an important reference index in the field of fluid transportation. When fluid flows around a hydrofoil, it generates friction drag and pressure drag, greatly reducing the hydrofoil's hydraulic performance. This study designs a bionic drag reduction structure by arranging fish scales on a Clark-Y hydrofoil. The overlapping size, thickness, and coverage area of fish scales are taken as design parameters, and the optimal design scheme is attained by using the Taguchi method. Large eddy simulation is used to numerically simulate various schemes. Results show that when the overlapping size O is 2.00 mm, the thickness h is 0.36 mm, the initial position x/C of the fish scale covering is 0 (where C is the chord length of the hydrofoil), and the hydrofoil exhibits excellent drag reduction performance. The total drag reduction rate of the hydrofoil is up to 35.15%, and the drag reduction rate of friction drag and pressure drag is up to 39.56% and 25.64%, respectively. The lift–drag ratio of the hydrofoil increases by 18.04%. The bionic fish scale structure effectively inhibits turbulence, thereby reducing the drag caused by the transformation of laminar flow to turbulence. [ABSTRACT FROM AUTHOR]

In road extraction task, traditional squeeze-and-excitation (SE) module only calculates the mean value of each channel to represent the salient features of the roads, but it easily causes false detection due to the interference, such as water, roofs, and so on. This letter specifically proposes a multiple-parameters-guided SE (MPGSE) module for road extraction by incorporating two key parameters of the variance and the coefficient of variation into the SE module. Furthermore, MPGSE module adaptively adjusts the weights of different features to suppress the redundant information while enhancing the informative features, which makes the roads more separable from other disturbances. MPGSE greatly increases the between-class distance and decreases the within-class distance, thus enhancing the separation capability of the road features compared with other interference. In addition, MPGSE module is integrated into D-LinkNet to optimally fuse features, thus further improving the completeness of road feature representation. Undoubtedly, MPGSE integrated D-LinkNet (MPGSE-D-LinkNet) can achieve better road extraction performance than the other methods. The superiority of MPGSE-D-LinkNet is verified on the RoadNet benchmark dataset (RNBD) and Massachusetts road dataset. [ABSTRACT FROM AUTHOR]

This article proposes an automatic identification system (AIS) data aided Rayleigh constant false alarm rate (AIS-RCFAR) ship detection algorithm of multiple-target environment in synthetic aperture radar (SAR) images. This method aims to improve the detection performance in complex environment with the aid of AIS data. Traditional CFAR detectors generally use all the samples in the local background window for parameter estimation. However, in multiple-target environment, clutter edges and transition areas, due to the interference of the high-intensity outliers, such as target pixels, ghosts, and other interfering pixels, the parameters are often overestimated, causing degradation of the detection performance. Aiming at solving this problem, AIS-RCFAR designs an adaptive-threshold based clutter trimming method with an adaptive-trimming-depth aided by AIS data to effectively eliminate the high-intensity outliers in the local background window while greatly sustaining the real sea clutter samples. Maximum-likelihood-estimator with a closed-form solution is proposed to precisely estimate the parameters using the adaptively-trimmed clutter samples, the probability density function of the sea clutter following Rayleigh distribution can be accurately modeled. AIS-RCFAR greatly enhances the detection rate in both homogeneous and nonhomogeneous multiple-target environment, it also achieves a very low false alarm rate. In addition, the whole procedure of AIS-RCFAR is simple and efficient. Simulated data and real SAR images with corresponding matched AIS data are used for experiments to validate the superiority and feasibility of AIS-RCFAR. [ABSTRACT FROM AUTHOR]

In order to more efficiently mine the features of polarimetric synthetic aperture radar (PolSAR) and establish a more appropriate classification model, this article proposes an improved convolutional autoencoder (ICAE) based on texture feature fusion (TFF-ICAE) for PolSAR terrain classification. First, TFF-ICAE specifically designs a multi-indicator squeeze-and-excitation (MI-SE) block and incorporates it into the CAE network. MI-SE can enhance the essential feature information while suppressing the interference information as much as possible, and it can effectively increase the between-class distance while reducing the within-class distance. Then, TFF-ICAE uses gray level co-occurrence matrix (GLCM) to capture the texture features, and it optimally fuses these texture features and the deep features extracted by ICAE to complete the multilevel feature fusion, elevating the feature representation completeness of the terrain. That is, TFF-ICAE effectively enhances the feature separation capability of different categories while greatly elevating the feature representation completeness. Experiments on the datasets of San Francisco, Oberpfaffenhofen, and Flevoland show that the proposed TFF-ICAE, respectively, achieves overall accuracies of 93.44%, 97.61%, and 97.78%, which are at least 0.92%, 1.52%, and 0.97% higher than other algorithms. Undoubtedly, the superiority of TFF-ICAE is verified on these datasets. [ABSTRACT FROM AUTHOR]

An adaptive-trimming-depth-based constant false alarm rate (ATD-CFAR) ship detector of heterogeneous environment in SAR imagery is proposed in this letter. Traditional CFAR detectors generally use all samples in the background window for parameter estimation. However, in the heterogeneous regions, these detectors will overestimate the parameters used for statistical modelling due to the interference of high-intensity interference pixels such as adjacent ships, ghosts, breakwaters and azimuth ambiguity, which leads to the missing detection of ship targets. To solve this problem, we design an adaptive-depth-based method for clutter trimming in the local reference window, so the interference pixels can be effectively removed, while the real sea clutter samples can be retained to the greatest extent. After that, the maximum likelihood estimator is used for parameter estimation, where the estimation accuracy is greatly elevated, and statistical model of the sea clutter is precisely established. [ABSTRACT FROM AUTHOR]

Effective utilization of structural information is important for high-resolution synthetic aperture radar (SAR) image change detection. For comprehensively utilizing the local and global structures in SAR images, a hierarchical spatial-temporal graph kernel (STGK) method is proposed in this paper for high-resolution SAR image change detection. First, the bi-temporal hierarchical graph models are constructed for extracting the local-global structures in the bi-temporal SAR images. Then, a STGK function, which measures the spatial and temporal similarities between the local-global structures, is constructed for indicating the change levels between the bi-temporal images. Finally, a support vector machine (SVM) is implemented with the STGK function for producing the final change detection results. Experimental results on real GaoFen-3 SAR data sets demonstrate the effectiveness of the proposed method, and prove that the STGK method is capable of detecting changed areas with relatively complex structures. [ABSTRACT FROM AUTHOR]

This paper proposes an outliers-robust constant false-alarm rate (OR-CFAR) detector of Gaussian clutter based on the truncated-maximum-likelihood estimator (TMLE) in SAR imagery. The proposed method aims at elevating the detection performance in multiple-target environment, where the sea clutter samples are often contaminated by the interfering target pixels, the azimuth ambiguities, and the breakwater. As a consequence, the parameters used for statistical modeling are over-estimated, resulting in a degradation of the CFAR detection rate. Inspired by the traditional two-parameter CFAR (TP-CFAR) detector of Gaussian clutter, OR-CFAR designs an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers from the clutter samples in the local reference window, and the probability density function (PDF) of the sea clutter can be accurately modeled through the newly raised TMLE. Furthermore, the optimal truncation depth used for clutter truncation and PDF modeling is evaluated and selected properly to get the best detection results. The OR-CFAR greatly enhances the CFAR detection rate in multiple-target environment, and it is computationally simple and efficient, which has a great application value. The Chinese Gaofen-3 SAR data are used for experiments to show the better detection performance of OR-CFAR. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-scale rotation-invariant haar-like (MSRI-HL) feature integrated convolutional neural network (MSRIHL-CNN)-based ship detection algorithm of the multiple-target environment in synthetic aperture radar (SAR) imagery. Usually, ship detection includes preprocessing, prescreening, discrimination, and classification. Among them, prescreening and discrimination are the most two important stages so that they catch great intention. Based on our previous work, we propose a truncated-clutter-statistics-based joint, constant false alarm rate (CFAR) detector (TCS-JCFAR) for ship target prescreening in the multiple-target environment. TCS-JCFAR greatly enhances the prescreening rate in the multiple-target environment while achieving a low observed FAR. In the discrimination stage, conventional CNN extracts the deep features (high-level features); however, it will lose the local texture and edge information (low-level features) which are of great significance for target discrimination. Hence, the MSRI-HL features are used to represent the multi-scale, rotation-invariant texture, and edge information that conventional CNN fails to capture. The extracted low-level MSRI-HL features and the high-level deep features are optimally fused to a multi-layered feature vector. Finally, the multi-layered feature vector is fed into a typical support vector machine (SVM) classifier for ship target discrimination. The proposed MSRIHL-CNN combines the low-level texture and edge features and the high-level deep features; moreover, they are optimally fused to fully represent the ship targets. Undoubtedly, MSRIHL-CNN has better discrimination performance. The superiority of the proposed TCS-JCFAR-based prescreener and MSRIHL-CNN-based discriminator is validated on the Chinese Gaofen-3 SAR imagery. [ABSTRACT FROM AUTHOR]

The performance of synthetic aperture radar (SAR) image classification based on a conventional convolutional neural network (CNN) is limited by a trade-off between immunity to speckle noise and the ability to locate boundaries accurately. Difficulties regarding the accurate location of boundaries are a result of the smoothing effect of the pooling layer. To address this issue, we propose a novel framework called SRAD-CNN for SAR image classification. In this framework, we apply a filtering layer constructed according to prior knowledge of the speckle reducing anisotropic diffusion (SRAD) filter. The filtering layer can not only reduce speckle but also enhance the boundaries. The main parameter that controls the degree of filtering can be optimized adaptively by a backpropagation algorithm. Image patches adaptively filtered by the filtering layer are then put into the CNN layers to assign a label. Due to the effect of the filtering layer, for our proposed SRAD-CNN, both the speckle noise immunity and the sensitivity to boundaries are superior to those of conventional CNN.To confirm the performance of the proposed SRAD-CNN, we conducted experiments using both simulated and real SAR images. The experimental results demonstrated that the parameter of the filtering layer could be optimized adaptively for different scenes, different noise levels, and different image resolutions. The SRAD-CNN outperformed the conventional CNN in both overall classification accuracy and maintenance of boundary accuracy on images with different resolutions and noise levels with limited training samples. [ABSTRACT FROM AUTHOR]

The convolutional neural network (CNN)-based pixel-wise synthetic aperture radar (SAR) data classification does not take fully use the spatial neighborhood information due to the fact that the impact of neighborhood pixels is not taken into consideration. The flaw of CNN-based classification method may lead to misclassification under some conditions. In this paper, we propose a novel adaptive neighborhood-based convolutional neural network (AN-CNN) for the single polarimetric synthetic aperture radar data classification. In the convolution layer, the neighborhood pixels are adaptively weighted based on their bilateral distance (spatial and feature distance) to the central pixel. In this way, different pixels have different impact on the classification result of the central pixel. The spatial distance-based weighting can reduce the misclassifications in the homogenous regions which are caused by speckle noise and the feature distance-based weighting is beneficial for the classification in the boundary regions. As a result, the misclassification is obviously reduced by the proposed AN-CNN which has a new cost function. Experimental results on simulated and real SAR data show that our proposed AN-CNN can notably improve the classification accuracy in both boundary regions and homogeneous regions compared with conventional CNN in different scenes especially when limited training samples are explored. [ABSTRACT FROM AUTHOR]

Traditional constant false alarm rate (CFAR) detectors suffer probability of detection (PD) degradation influenced by the outliers such as interfering ship targets, side lobes, and ghosts, especially in crowded harbors and busy shipping lines. In this paper, a new two-parameter CFAR detector based on adaptively truncated clutter statistics (TS-LNCFAR) is proposed. The new two-parameter CFAR detector uses log-normal as the statistical model; by adaptive-threshold-based clutter truncation in the background window, the outliers are removed from the clutter samples, while the real clutter is preserved to the largest degree. The log-normal model is accurately built using the truncated clutter statistics through the maximum-likelihood estimator. Compared with traditional CFAR detectors, the parameter estimation is more accurate, and TS-LNCFAR has a better false alarm regulation property and a high PD in a multiple-target environment. Furthermore, the parameter estimation and threshold calculation do not need iterative numerical calculation, and TS-LNCFAR has a high computational efficiency. The superiority of the proposed TS-LNCFAR detector is validated on the multilook Envisat-ASAR and TerraSAR-X data. [ABSTRACT FROM PUBLISHER]

The characteristic difference between targets and clutter is analyzed. Considering the ship target's gray intensity distribution and its shape difference compared to the clutter, in this letter, a new algorithm is presented based on correlation. The algorithm utilizes the strong gray intensity correlation in the ship target; also, the joint gray intensity distribution using 2-D joint log-normal distribution of a pixel with neighboring pixels in the clutter is modeled, which can be used for correlation-based joint constant false alarm rate detection. Using this algorithm, the false alarms caused by speckle and local background nonhomogeneity can be greatly reduced. The detection performance is much better. [ABSTRACT FROM PUBLISHER]

Steel strip acts as a fundamental material for the steel industry. Surface defects threaten the steel quality and cause substantial economic and reputation losses. Roll marks, always occurring periodically in a large area, are put on the top of the list of the most serious defects by steel mills. Essentially, the online roll mark detection is a tiny target inspection task in high-resolution images captured under harsh environment. In this paper, a novel method—namely, Smoothing Complete Feature Pyramid Networks (SCFPN)—is proposed for the above focused task. In particular, the concept of complete intersection over union (CIoU) is applied in feature pyramid networks to obtain faster fitting speed and higher prediction accuracy by suppressing the vanishing gradient in training process. Furthermore, label smoothing is employed to promote the generalization ability of model. In view of lack of public surface image database of steel strips, a raw defect database of hot-rolled steel strip surface, CSU_STEEL, is opened for the first time. Experiments on two public databases (DeepPCB and NEU) and one fresh texture database (CSU_STEEL) indicate that our SCFPN yields more competitive results than several prestigious networks—including Faster R-CNN, SSD, YOLOv3, YOLOv4, FPN, DIN, DDN, and CFPN. [ABSTRACT FROM AUTHOR]

In multiple-input multiple-output synthetic aperture radar (MIMO–SAR) signal processing, a reliable separation of multiple transmitted waveforms is one of the most important and challenging issues, for the unseparated signal will degrade the performance of most MIMO–SAR applications. As a solution to this problem, a novel APC–MIMO–SAR system is proposed based on the azimuth phase coding (APC) technique to transmit multiple waveforms simultaneously. Although the echo aliasing occurs in the time domain and Doppler domain, the echoes can be separated well without performance degradation by implementing the azimuth digital beamforming (DBF) technique, comparing to the performance of the orthogonal waveforms. The proposed MIMO–SAR solution based on the APC waveforms indicates the feasibility and the spatial diversity of the MIMO–SAR system. It forms a longer baseline in elevation, which gives the potential to expand the application of MIMO–SAR in elevation, such as improving the performance of multibaseline InSAR and three-dimensional SAR imaging. Simulated results on both a point target and distributed targets validate the effectiveness of the echo separation and reconstruction method with the azimuth DBF. The feasibility and advantage of the proposed MIMO–SAR solution based on the APC waveforms are demonstrated by comparing with the imaging result of the up- and down-chirp waveforms. [ABSTRACT FROM AUTHOR]