SAR Ship Target Recognition Via Multi-Scale Feature Attention and Adaptive-Weighed Classifier

Maritime surveillance is indispensable for civilian fields, including national maritime safeguarding, channel monitoring, and so on, in which synthetic aperture radar (SAR) ship target recognition is a crucial research field. The core problem to realizing accurate SAR ship target recognition is the large inner-class variance and inter-class overlap of SAR ship features, which limits the recognition performance. Most existing methods plainly extract multi-scale features of the network and utilize equally each feature scale in the classification stage. However, the shallow multi-scale features are not discriminative enough, and each scale feature is not equally effective for recognition. These factors lead to the limitation of recognition performance. Therefore, we proposed a SAR ship recognition method via multi-scale feature attention and adaptive-weighted classifier to enhance features in each scale, and adaptively choose the effective feature scale for accurate recognition. We first construct an in-network feature pyramid to extract multi-scale features from SAR ship images. Then, the multi-scale feature attention can extract and enhance the principal components from the multi-scale features with more inner-class compactness and inter-class separability. Finally, the adaptive weighted classifier chooses the effective feature scales in the feature pyramid to achieve the final precise recognition. Through experiments and comparisons under OpenSARship data set, the proposed method is validated to achieve state-of-the-art performance for SAR ship recognition

Absolute surface metrology by shear rotation with position error correction

Background: Absolute test is one of the most important and efficient techniques to saperate the reference surface which usually limits the accuracy of test results. Method: For the position error correction in absolute interferometry tests based on rotational and translational shears, the estimation algorithm adopts least-squares technique to eliminate azimuthal errors caused by rotation inaccuracy and the errors of angular orders are compensated with the help of Zernike polynomials fitting by an additional rotation measurement with a suitable selection of rotation angles. Results: Experimental results show that the corrected results with azimuthal errors are very close to those with no errors, compared to the results before correction. Conclusions: It can be seen clearly that the testing errors caused by rotation inaccuracy and alignment errors of the measurements can be consequently eliminated from the differences in measurement results by the proposed method

Hydrodynamic Behaviors and Geochemical Evolution of Groundwater for Irrigation in Yaoba Oasis, China

The Yaoba Oasis is an irrigated cropland entirely dependent on groundwater; previous investigations (1980–2015) revealed an over-abstraction of groundwater and deteriorating groundwater quality. For further exploring the hydrodynamic behaviors and geochemical processes of groundwater during the irrigation season, groundwater samples were collected and analyzed using different techniques including classical statistics, correlation analysis, Piper diagrams, and Gibbs diagrams. The results indicated that Na+, K+, SO42− and Cl− were the main ions in groundwater, which were significantly correlated with TDS. The water–rock interaction is manifested by the precipitation of calcite and dolomite and the dissolution of rock salt and gypsum as an increase in TDS related to evaporation. In addition, the increasing complexity of hydrochemical type is caused by the rapid variation of hydrodynamic regime, irrigation and evaporation, which are subjected to the constraints of salty water intrusion from the desert salty lake and infiltration of irrigation return flow. Existing wells should limit overexploitation to halt the decline in groundwater levels and cut down irrigation water to reduce the risk of groundwater contamination and restore ecological balance in desert oasis

Synthetic Aperture Radar Processing Approach for Simultaneous Target Detection and Image Formation

Finding out interested targets from synthetic aperture radar (SAR) imagery is an attractive but challenging problem in SAR application. Traditional target detection is independent on SAR imaging process, which is purposeless and unnecessary. Hence, a new SAR processing approach for simultaneous target detection and image formation is proposed in this paper. This approach is based on SAR imagery formation in time domain and human visual saliency detection. First, a series of sub-aperture SAR images with resolutions from low to high are generated by the time domain SAR imaging method. Then, those multiresolution SAR images are detected by the visual saliency processing, and the corresponding intermediate saliency maps are obtained. The saliency maps are accumulated until the result with a sufficient confidence level. After some screening operations, the target regions on the imaging scene are located, and only these regions are focused with full aperture integration. Finally, we can get the SAR imagery with high-resolution detected target regions but low-resolution clutter background. Experimental results have shown the superiority of the proposed approach for simultaneous target detection and image formation

Ship Detection from Ocean SAR Image Based on Local Contrast Variance Weighted Information Entropy

Ship detection from synthetic aperture radar (SAR) images is one of the crucial issues in maritime surveillance. However, due to the varying ocean waves and the strong echo of the sea surface, it is very difficult to detect ships from heterogeneous and strong clutter backgrounds. In this paper, an innovative ship detection method is proposed to effectively distinguish the vessels from complex backgrounds from a SAR image. First, the input SAR image is pre-screened by the maximally-stable extremal region (MSER) method, which can obtain the ship candidate regions with low computational complexity. Then, the proposed local contrast variance weighted information entropy (LCVWIE) is adopted to evaluate the complexity of those candidate regions and the dissimilarity between the candidate regions with their neighborhoods. Finally, the LCVWIE values of the candidate regions are compared with an adaptive threshold to obtain the final detection result. Experimental results based on measured ocean SAR images have shown that the proposed method can obtain stable detection performance both in strong clutter and heterogeneous backgrounds. Meanwhile, it has a low computational complexity compared with some existing detection methods

A Superfast Super-Resolution Method for Radar Forward-Looking Imaging

The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable L1 regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem. However, its real-time imaging ability is limited to matrix inversion and iterations. Although previous studies have used the special structure of the coefficient matrix to reduce the computational complexity of each iteration, the real-time performance is still limited due to the need for hundreds of iterations. In this paper, a superfast SBA (SFSBA) is proposed to overcome this shortcoming. Firstly, the super-resolution problem is transmitted into an L1 regularization problem in the framework of regularization. Then, the proposed SFSBA is used to solve the nondifferentiable L1 regularization problem. Different from the traditional SBA, the proposed SFSBA utilizes the low displacement rank features of Toplitz matrix, along with the Gohberg-Semencul (GS) representation to realize fast inversion of the coefficient matrix, reducing the computational complexity of each iteration from O(N3) to O(N2). It uses a two-order vector extrapolation strategy to reduce the number of iterations. The convergence speed is increased by about 8 times. Finally, the simulation and real data processing results demonstrate that the proposed SFSBA can effectively improve the azimuth resolution of radar forward-looking imaging, and its performance is only slightly lower compared to traditional SBA. The hardware test shows that the computational efficiency of the proposed SFSBA is much higher than that of other traditional super-resolution methods, which would meet the real-time requirements in practice

A Novel Flickering Multi-Target Joint Detection Method Based on a Biological Memory Model

The robust target detection ability of marine navigation radars is essential for safe shipping. However, time-varying river and sea surfaces will induce target scattering changes, known as fluctuating characteristics. Moreover, the targets exhibiting stronger fluctuation disappear in some frames of the radar images, which is known as flickering characteristics. This phenomenon causes a severe decline in the detection performance of traditional detection methods. A biological memory model-based dynamic programming multi-target joint detection method was proposed to address this issue in this paper. Firstly, a global detection operator is used to discretize the multi-target state into multiple single-target states, achieving the discretization of numerous targets. Meanwhile, updating the formula of the memory weight merit function can strengthen the joint frame correlation of the flickering characteristics target. The progressive loop integral is utilized to update the target states to optimize the candidate target set. Finally, a two-stage threshold criterion is utilized to detect the target at different amplitude levels accurately. Simulation and experimental results are given to validate the assertion that the detection performance of the proposed method is greatly improved under a low SCR of 3-8 dB for multiple flickering target detection

Multiview Deep Feature Learning Network for SAR Automatic Target Recognition

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions

Radar Operation Mode Recognition via Multifeature Residual-and-Shrinkage ConvNet

Radar operation mode recognition holds an increasingly critical place in electronic countermeasure as well as in remote sensing. However, the overlapped waveform parameters pose huge challenges to performing the radar operation mode recognition task in severe electromagnetic environments, particularly with large measurement errors or small sample lengths. By analyzing the timing patterns of a single radar pulse parameter and the correlation characteristics of multiple radar pulse parameters, this article first provides a revolutionary representation of the radar operating state by integrating interpulse parameter characteristics. Subsequently, a multifeature fused stream-level recognition framework with an attention mechanism, named residual-and-shrinkage ConvNet, is proposed to identify typical radar operating states. This tailored-made deep learning framework can effectively extract the timing and correlative features, which are conducive to pattern classification. The results of numerical experiments suggest that the proposed approach affords superior performance for the operation mode recognition task, even when the measurement error is large and the sample length is small, signifying the proposed method is strongly robust and time-efficient

Slow-Time Ambiguity Function Shaping With Spectral Coexistence for Cognitive Radar

In the spectrum-congested environment, weak target obscuration can be addressed by simultaneously shaping the ambiguity function and waveform spectrum based on cognitive radar a priori information. In this article, we investigate joint waveform design to suppress slow-time ambiguity function (STAF) disturbance power and overlapping frequency band energy. Thus, a novel design criterion involving minimizing the interested range-Doppler blocks of STAF and energy spectrum density stopband is constructed, which is subject to the waveform energy and peak-to-average ratio constraints. To cope with the resulting complex quartic optimization problem, a waveform design approach is proposed which utilizes the iterative sequential quartic optimization algorithm framework to obtain a closed-form solution at each iteration. Finally, the designed waveforms can suppress the interested range-Doppler block level while satisfying the spectral coexistence requirements. Numerical simulation results verify that the proposed method has higher STAF disturbance suppression performance than state-of-the-art methods. Meanwhile, this method also possesses the ability to reject narrow-band spectrum interference