Loading [MathJax]/jax/output/SVG/jax.js
WU Wenjun, TANG Bo, TANG Jun, et al. Waveform design for dual-function radar-communication systems in clutter[J]. Journal of Radars, 2022, 11(4): 570–580. doi: 10.12000/JR22105
Citation: SUN Xiaokun, YUN Zekai, HU Canbin, et al. End-to-end registration algorithm for high-resolution multi-view SAR images[J]. Journal of Radars, 2025, 14(2): 389–404. doi: 10.12000/JR24211

End-to-end Registration Algorithm for High-resolution Multi-view SAR Images

DOI: 10.12000/JR24211 CSTR: 32380.14.JR24211
Funds:  The Fundamental Research Funds for the Central Universities (buctrc202218), The Fundamental Research Funds for the Central Universities (ZY2413)
More Information
  • Corresponding author: HU Canbin, canbinhu@163.com
  • Received Date: 2024-10-22
  • Rev Recd Date: 2024-12-12
  • Available Online: 2024-12-14
  • Publish Date: 2024-12-26
  • Due to the side-looking and coherent imaging mechanisms, feature differences between high-resolution Synthetic Aperture Radar (SAR) images increase when the imaging viewpoint changes considerably, making image registration highly challenging. Traditional registration techniques for high-resolution multi-view SAR images mainly face issues, such as insufficient keypoint localization accuracy and low matching precision. This work designs an end-to-end high-resolution multi-view SAR image registration network to address the above challenges. The main contributions of this study include the following: A high-resolution SAR image feature extraction method based on a local pixel offset model is proposed. This method introduces a diversity peak loss to guide response weight allocation in the keypoint extraction network and optimizes keypoint coordinates by detecting pixel offsets. A descriptor extraction method is developed based on adaptive adjustment of convolution kernel sampling positions that utilizes sparse cross-entropy loss to supervise descriptor matching in the network. Experimental results show that compared with other registration methods, the proposed algorithm achieves substantial improvements in the high-resolution adjustment of convolution kernel sampling positions, which utilize sparse cross-entropy loss to supervise descriptor matching in the network. Experimental results illustrate that compared with other registration methods, the proposed algorithm achieves remarkable improvements in high-resolution multi-view SAR image registration, with an average error reduction of over 65%, 3~5-fold increases in the number of correctly matched point pairs, and an average reduction of over 50% in runtime.

     

  • Frequency Diverse Arrays (FDA) radar has recently drawn much attention among the researchers. FDA differs from the traditional phased array by using a small frequency increment across the array elements, which results in a range-angle-dependent beam pattern. FDA radar system is first proposed in Ref. [1]. In FDA radar, a uniform interelement frequency offset is applied across the array elements. FDA radar with uniform small and large frequency offset frequency has been investigated in Refs. [17]. Small frequency offset has been exploited to generate range-dependent beampattern, while large frequency offset can get independent echoes from the target.

    Unlike the phased array, the range-angle dependency of the FDA beampattern allows the radar system to focus the transmit energy in a desired range-angle space. This unique feature of FDA helps to suppress the range-dependent interferences[8] and increases the received SINR consequently. Especially for the mainlobe interference and clutter, the FDA can achieve a significant improvement in SINR against the phased array because the FDA provides the increased Degrees Of Freedom (DOFs) in range domain. However, the FDA beampattern is shown to be periodic in range and time[2], which goes to maximum at multiple time and range values. With this multiple-maximum beampattern, the resulting SINR will be deteriorated when the interferers are located at any of the maxima. To improve SINR, FDA with Time-Dependent Frequency Offset (TDFO-FDA) was proposed to achieve a time-independent beampattern at the target location[9]. Nevertheless, the proposed beampattern is still periodic in range which will result in the loss of SINR. A nonuniformly spaced linear FDA with linear incremental frequency increment has also been studied in Ref. [10], and a nonrepeating beampattern has been obtained for range-angle imaging of targets. A uniformly spaced linear FDA with Logarithmically (Log-FDA) increasing frequency offset is proposed in Ref. [11]. The proposed strategy provides a nonperiodic beampattern with the single-maximum in space. In Ref. [12], the beampattern of FDA who transmits the pulsed signal has been studied. Lately, few more publications have done some work in decoupling the range-angle dependent beampattern of FDA[1316]. All these papers only address the properties of the FDA beampattern, and they do not study the common rule for the FDA configuration to form a single-maximum transmit beampattern.

    With the pioneer work on FDA radar, we aim to decouple the range and angle in the beampattern and provide a nonperiodic beampattern with the single-maximum in the illuminated range-angle space. In this paper, we propose a basic criteria for the FDA configuration, in which the element spacing and frequency increment are configurable, to form a single-maximum beampattern through mathematical analysis. This single-maximum beampattern, unlike the multiple-maxima beampattern, can help to further suppress range-dependent interferences, causing improved SINR and increased detect ability. The proposed rule for the FDA configuration will be helpful to design the FDA.

    The rest of the paper is organized as follows. In Section 2, the basic FDA model has been described and the basic criterion is derived for the FDA configuration to form a single-maximum beampattern through mathematical analysis. Moreover, several specific conditions are introduced. In Section 3, the beampattern has been plotted for the specific conditions discussed in Section 2. Finally, in Section 4 we conclude the paper.

    Consider an array of M transmit elements, we assume that the waveform radiated from each antenna element is identical with a frequency increment, as shown in Fig. 1. The radiated frequency from the m-th element is

    Figure  1.  FDA configuration
    fm=f0+Δfm, m=0,···,M1 (1)

    where Dfm is the frequency increment of m-th element with reference to the carrier frequency f0. Specifically, Df0 = 0.

    Considering a given far-field point, the phase of the signal transmitted by the m-th element can be represented by

    ψm=2πfm(trmc) (2)

    where c and rm are the speed of light and the distance between the m-th element and the observed point, respectively.

    The range difference between individual elements is approximated by

    rm=r0dmsinθ0 (3)

    where θ0 is the desired angle, dm is the spacing between the m-th element and the first element. Specifically, d0 = 0.

    So the phase difference between the m-th element and the first element is

    Δψm=ψmψ0=2π(fm(trmc)f0(tr0c))=2πΔfmt2πfmdmsinθ0c+2πΔfmr0c (4)

    In Eq. (4), the third term is important because it shows that the FDA radiation pattern depends on both the range and the frequency increment. Taking the first element as the reference for the array, the steering vector can be expressed as

    a(θ,r,t)=[1,···,exp(j2πΔfm(tc+r)+fmdmsinθc)]T (5)

    where [·]T denotes the transpose operator.

    In the pusled-FDA, for t[Te2,Te2] , Te is the pulsewidth, the maximum value of phase variance[12] during the pulse duration can be derived as

    ξ=maxm2πΔfmTe (6)

    When the phase variance ξ is small enough, the beampattern of the pulsed-FDA can be viewed as quasi-static. Actually, in practical radar systems, duty cycle other than pulse duration is often used to describe the characterization of the pulsed-waveform. Then, the phase variance ξ can be further written as

    |ξ|=maxm2π|Δfm|frdt=2πdtmaxmρm1 (7)

    where dt is the duty cycle, which is usually small. Eq. (7) holds when maxmρm is also small.

    So under the condition Eq. (7), the time t can be neglected. So Eq. (5) can be simplified as

    a(θ,r)=[1,···,exp(j2πΔfmr+fmdmsinθc)]T (8)

    Throughout this paper, we assume a narrow-band system where the propagation delays manifest as phase shifts to the transmitted signals and Eq. (7) is satisfied. To steer the maximum at an expected target location ( θ 0, r0), the complex weights are configured as a( θ 0, r0), so the transmit beampattern can be expressed

    AF(θ,r)=|aH(θ0,r0)a(θ,r)|=|M1m=0exp(j2πΔfm(rr0)fmdm(sinθsinθ0)c)|

    (9)

    where [·]H denotes the conjugate transpose operator.

    It is easy to see that the beam direction will vary as a function of the range and angle, which means the beampattern is range-angle dependent. Since the beampattern is coupled in the range and angle, the target’s range and angle cannot be estimated directly by the FDA beamformer output. Note that the beampattern is also related to Dfm and dm, so the desired single-maximum beampattern can be obtained by setting the proper Dfm and dm.

    In Eq. (9), when m = 0, the exponential term is equal to 1. To obtain the maximum value of the beampattern, the exponential terms should be all equal to 1 for m=1,2,···,M1 . So the phase of the exponential term should be the integral multiple of 2π , which can be expressed as

    Δfm(rr0)f0dm(sinθsinθ0)Δfmdm(sinθsinθ0)c=Lm (10)

    where Lm is an integer, e.g. Lm = 0, ±1,···, m=1,2,···,M1 .

    The Eq. (10) can be rewritten as

    r=Lmc+f0dm(sinθsinθ0)Δfm+dm(sinθsinθ0)+r0 (11)

    Since dm(sinθsinθ0)r0 , the term dm(sinθ sinθ0) in Eq. (8) can be neglected. The curves formed by Eq. (11) will be called as range-angle distribution curves throughout the paper. Then Eq. (11) can be approximately expressed as

    Δfm(rr0)f0dm(sinθsinθ0)=Lmc, m=1,2,···,M1 (12)

    Rewrite Eq. (12) into matrix form as

    Ax=b (13)

    where A=[Δf1f0d1Δf2f0d2ΔfM1f0dM1] , x=[rr0sinθsinθ0] , b=[L1cL2cLM1c] , Lm = 0, ±1, ···, m=1,2,···, M – 1.

    To decouple the range and angle, the beam-pattern should have the unique maximum point in the range-angle distribution diagram, which means the Eq. (13) has the unique solution ( θ 0, r0). The necessary and sufficient condition of that the Eq. (13) has the unique solution is

    rank(A)=rank(˜A)=2 (14)

    where rank (·) is the rank of a matrix, ˜A=(A,b) . When rank(A)=rank(˜A)=1 , the Eq. (13) has infinite solutions, corresponding to the conventional FDA condition, which will be discussed in detail later.

    To satisfy rank (A) = 2, dmPΔfm , P is a constant.

    To satisfy rank(A)=rank(˜A) , Lm=Qdm or Lm=SΔfm , Lm is an integer, and assume that Q, S are the minimum non-zero constants to satisfy the equations. Since dmPΔfm must be satisfied, the two equations cannot be hold at the same time but when Lm = 0, m=1,2,···, M–1.

    In the following, under the condition of dmPΔfm,Lm=0,±1 ,···, we make a summary with different parameter configurations:

    (1) when Lm = 0, the Eq. (13) has the unique solution ( θ 0, r0);

    (2) when Lm=Qdm0 , LmSΔfm , the Eq. (10) has the solutions (arcsin(sin(θ0) kQλ0),r0),λ0=cf0,k=0,±1,±2,··· . When |sin(θ0)kQλ0|1 , the angle grating lobes will occur at angle arcsin(sin(θ0)kQλ0) in the beampattern. Otherwise, the Eq. (13) has no solution, resulting in no angle grating lobes;

    (3) when Lm=SΔfm0,LmQdm , the Eq. (13) has the (θ0,r0+Skc) , k=0,±1,±2,··· , which means the range grating lobes will occur at r0+Skc in the beampattern.

    Note that in array theory, when the adjacent element spacing is less than half the wavelength, the angle grating lobes will never appear. If Qλ0=±1 and θ0=0 , where the element spacing is λ 0, then the grating lobes will occur at angle ±90°. But Lm=SΔfm can always be satisfied since Lm is an integer whose range is [,+] . The range grating lobes will always occur at range r0+Skc in the beampattern. The position of the range grating lobe changes with different S. For example, S = 0.01, the distance between the grating lobe and the mainlobe is 3000 km. So if r0±Sc is out of the illuminated range space [Rmin,Rmax] , the beampattern has a single-maximum point ( θ 0, r0) in the illuminated range space, which means the range and angle are decoupled.

    So we can conclude that the beampattern of the FDA is always range-periodic, the grating lobes will always occur at range r0+Skc , k=0,±1,±2,··· . To obtain the single-maximum beampattern in the illuminated range space [Rmin,Rmax] , the designing criteria for the FDA is dmPΔfm , and 2Sc>RmaxRmin , |sin(θ0)±Qλ0|>1 , k=0,±1,±2,··· , Lm=0,±1,··· , m=1,2,··· ,M – 1, P is a constant, Q, S are the minimum non-zero constants to satisfy the equations Lm= SΔfm0 and Lm=Qdm0 .

    Once the range and angle is decoupled, the target’s range and angle can be estimated directly by the FDA beamformer output. Also the 2-dementional MUSIC algorithm[16] for estimating the target’s range and angle can be used as well.

    For the conventional FDA, Δfm=mΔf , dm = md, Df and d are configurable parameters to control the frequency increment and the element spacing. When Lm = nm, n=0,±1,±2,··· , m=1,2,···,M1 , we can get rank(A)=rank (˜A)=1 . Under this circumstance, the Eq. (13) has infinite solutions. The solutions form the range-angle dependent curves, which have expressions as:

    r=f0dΔfsinθf0dsinθ0Δf+r0+ncΔf (15)

    In Eq. (15), the expression is no longer related to m, which means the range-angle curve for different element coincides with each other, as depicted in Fig. 2(a). In the range-angle distri-bution diagram, the curve is periodic in range, and the range difference between the adjacent curves is c/Df. The corresponding beampattern is depicted as Fig. 3(a).

    For the Expf-FDA, whose frequency incre-ment is exponentially increased, Δfm=(bm 1)Δf , dm = md. b is a configurable constant. The range solution rises when b gets larger. The range-angle distribution curves and the beampattern are depicted in Fig. 2(b) and Fig. 3(b), respectively.

    Likewise, for the Logd-FDA, whose element spacing is logarithmically increased, Δfm=mΔf , dm=log(m+1)d. When Lm = nm, n=0,±1, ±2,··· , m=1,2,···,M1 , we can get rank(A)= rank(˜A)=2 , so the range grating lobes will occur in the beampattern as depicted in Fig. 3(c), and the range difference between the grating lobe and the mainlobe is c/Df. The corresponding range-angle distribution diagram is depicted in Fig. 2(c).

    For another kind of FDA, called Expf-Logd-FDA, where the frequency increment is exponentially increased and the element spacing is logarithmically increased, Δfm=(bm1)Δf , dm=log(m+1)d . The range-angle distribution curves and beampattern are depicted in Fig. 2(d) and Fig. 3(d), respectively.

    Bampattern expressed in Eq. (9) and the range-angle distribution curves expressed in Eq. (11) were simulated and plotted for different kinds of FDA discussed in Section 2. The results are discussed and compared with the different kinds of FDA. The illuminated range space is (0 km, 800 km]. To generate these plots, the values of the configurable parameters have been taken as listed in Tab. 1. To avoid angle grating lobes, the parameter d is less than half the wavelength.

    Table  1.  Parameters for simulations
    Parameter Value Parameter Value
    Element number M 8 d 0.1 m
    Reference frequency f0 1 GHz b 1.4
    {\Delta}f 1 kHz Desired point
    ( \theta_0, r0)
    (0°, 400 km)
     | Show Table
    DownLoad: CSV

    The range-angle distribution curves are de-picted in Fig. 2. The curves with different color represent the range-angle distribution for different elements except the reference element in the FDA. It is easy to see that the range-angle distribution of the elements are the same in conventional FDA, and the range difference between the adjacent curves is c/Df = 300 km, which is consistent with the analysis in Section 2. For the Expf-FDA and Expf-Logd-FDA, Δfm =(1.4m1)Δf , the range between the first grating lobe and the mainlobe is 3e6 km, which is not in the illuminated space. So the curves of the elements in the range-angle distribution diagram have the single intersection point, which means that the beampattern has the single-maximum point in the illuminated range space. But for the Logd-FDA, when Lm = nm, the distance between the grating lobe and the mainlobe is c/Df = 300 km according to the analysis in Section 2. So in the range-angle distribution diagram, the curves have 3 intersection points, the range difference between adjacent intersection points is 300 km.

    The beampatterns are depicted in Fig. 3. Similar to the range-angle curve in Fig. 2, the beampattern of the conventional FDA is a range-angle-dependent beampattern. For the Expf-FDA and Expf-Logd-FDA, the beampatterns have a single-maximum point in the illuminated space corresponding to the single intersection point in the range-angle distribution diagram. For the Logd-FDA, the beampattern has 3 range grating lobes corresponding to the 3 intersection points in the range-angle distribution diagram. For the latter 3 FDAs, the contour of the beampattern is an ellipse, which is because the range-angle distribution curves are tightly distributed. The direction of the major axis of the ellipse is corresponding to the most tightness distribution direction of the range-angle distribution curves.

    Figure  2.  The range-angle distribution diagram for different element

    In the meanwhile, we analyze the range and angle solutions of different FDAs in Fig. 4. The Expf-FDA and the Exp-Logd-FDA have the same range solution, and the range solution of conventional FDA and Logd-FDA are the same. That is because the bandwidth across the whole array is the same for each pair. The same situation occurs for the angle solution, if the arrays have the same array aperture, they have the same angle solutions.

    Figure  4.  Range and angle section views of beampattern

    We choose the target position at (400 km, 20°), and the transmit beampattern is depicted in Fig. 5. Similar to Fig. 3, we can see that the single maximum beam is formed at the target position.

    Figure  5.  Range versus angle normalized beampattern (target position at (400 km, 20°))
    Figure  3.  Range versus angle normalized beampattern

    In this paper, we propose a basic criteria for the FDA configuration to provide a single-maximum beampattern in the illuminated space. The single-maximum beampattern can be generated by configuring the element spacing and frequency increment of the FDA. Through the analysis, we can find out that the beampattern of FDA is always range-periodic. Results show that a single-maximum beampattern can be generated with the corresponding criteria by choosing the proper frequency increment. As this paper describes the transmitter only, designing an appropriate receiver is our future work.

  • [1]
    黄钟泠, 姚西文, 韩军伟. 面向SAR图像解译的物理可解释深度学习技术进展与探讨[J]. 雷达学报, 2022, 11(1): 107–125. doi: 10.12000/JR21165.

    HUANG Zhongling, YAO Xiwen, and HAN Junwei. Progress and perspective on physically explainable deep learning for synthetic aperture radar image interpretation[J]. Journal of Radars, 2022, 11(1): 107–125. doi: 10.12000/JR21165.
    [2]
    徐真, 王宇, 李宁, 等. 一种基于CNN的SAR图像变化检测方法[J]. 雷达学报, 2017, 6(5): 483–491. doi: 10.12000/JR17075.

    XU Zhen, WANG Yu, LI Ning, et al. A novel approach to change detection in SAR images with CNN classification[J]. Journal of Radars, 2017, 6(5): 483–491. doi: 10.12000/JR17075.
    [3]
    王志豪, 李刚, 蒋骁. 基于光学和SAR遥感图像融合的洪灾区域检测方法[J]. 雷达学报, 2020, 9(3): 539–553. doi: 10.12000/JR19095.

    WANG Zhihao, LI Gang, and JIANG Xiao. Flooded area detection method based on fusion of optical and SAR remote sensing images[J]. Journal of Radars, 2020, 9(3): 539–553. doi: 10.12000/JR19095.
    [4]
    洪文, 王彦平, 林赟, 等. 新体制SAR三维成像技术研究进展[J]. 雷达学报, 2018, 7(6): 633–654. doi: 10.12000/JR18109.

    HONG Wen, WANG Yanping, LIN Yun, et al. Research progress on three-dimensional SAR imaging techniques[J]. Journal of Radars, 2018, 7(6): 633–654. doi: 10.12000/JR18109.
    [5]
    丁赤飚, 刘佳音, 雷斌, 等. 高分三号SAR卫星系统级几何定位精度初探[J]. 雷达学报, 2017, 6(1): 11–16. doi: 10.12000/JR17024.

    DING Chibiao, LIU Jiayin, LEI Bin, et al. Preliminary exploration of systematic geolocation accuracy of GF-3 SAR satellite system[J]. Journal of Radars, 2017, 6(1): 11–16. doi: 10.12000/JR17024.
    [6]
    XIANG Yuming, PENG Lingxiao, WANG Feng, et al. Fast registration of multiview slant-range SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19(3): 4007505. doi: 10.1109/LGRS.2020.3045099.
    [7]
    WEI S and LAI Shanghong. Fast template matching based on normalized cross correlation with adaptive multilevel winner update[J]. IEEE Transactions on Image Processing, 2008, 17(11): 2227–2235. doi: 10.1109/TIP.2008.2004615.
    [8]
    WANG Fei and VEMURI B C. Non-rigid multi-modal image registration using cross-cumulative residual entropy[J]. International Journal of Computer Vision, 2007, 74(2): 201–215. doi: 10.1007/s11263-006-0011-2.
    [9]
    DELLINGER F, DELON J, GOUSSEAU Y, et al. SAR-SIFT: A SIFT-like algorithm for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 453–466. doi: 10.1109/TGRS.2014.2323552.
    [10]
    项德良, 徐益豪, 程建达, 等. 一种基于特征交汇关键点检测和Sim-CSPNet的SAR图像配准算法[J]. 雷达学报, 2022, 11(6): 1081–1097. doi: 10.12000/JR22110.

    XIANG Deliang, XU Yihao, CHENG Jianda, et al. An algorithm based on a feature interaction-based keypoint detector and sim-CSPNet for SAR image registration[J]. Journal of Radars, 2022, 11(6): 1081–1097. doi: 10.12000/JR22110.
    [11]
    LIAO Furong, CHEN Yan, CHEN Yunping, et al. SAR image registration based on optimized ransac algorithm with mixed feature extraction[C]. 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, USA, 2020: 1153–1156. doi: 10.1109/IGARSS39084.2020.9323180.
    [12]
    DENG Yang and DENG Yunkai. Two-step matching approach to obtain more control points for SIFT-like very-high-resolution SAR image registration[J]. Sensors, 2023, 23(7): 3739. doi: 10.3390/s23073739.
    [13]
    XIANG Deliang, XIE Yuzhen, CHENG Jianda, et al. Optical and SAR image registration based on feature decoupling network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5235913. doi: 10.1109/TGRS.2022.3211858.
    [14]
    XIANG Yuming, JIAO Niangang, LIU Rui, et al. A geometry-aware registration algorithm for multiview high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5234818. doi: 10.1109/TGRS.2022.3205382.
    [15]
    GUO Qiangliang, XIAO Jin, HU Xiaoguang, et al. Local convolutional features and metric learning for SAR image registration[J]. Cluster Computing, 2019, 22(2): 3103–3114. doi: 10.1007/s10586-018-1946-0.
    [16]
    FAN Jianwei, WU Yan, WANG Fan, et al. SAR image registration using phase congruency and nonlinear diffusion-based SIFT[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(3): 562–566. doi: 10.1109/LGRS.2014.2351396.
    [17]
    FAN Yibo, WANG Feng, and WANG Haipeng. A transformer-based coarse-to-fine wide-swath SAR image registration method under weak texture conditions[J]. Remote Sensing, 2022, 14(5): 1175. doi: 10.3390/rs14051175.
    [18]
    ELWAN M, AMEIN A S, MOUSA A, et al. SAR image matching based on local feature detection and description using convolutional neural network[J]. Security and Communication Networks, 2022, 2022(1): 5669069. doi: 10.1155/2022/5669069.
    [19]
    MEN Peng, GUO Hao, AN Jubai, et al. An improved L2Net for repetitive texture image registration with intensity difference heterogeneous SAR images[J]. Remote Sensing, 2022, 14(11): 2527. doi: 10.3390/rs14112527.
    [20]
    ZHANG Yifan, LI Zhiwei, WANG Wen, et al. A robust registration method for multi-view SAR images based on best buddy similarity[C]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Changsha, China, 2024: 881–886. doi: 10.5194/isprs-archives-XLVIII-1-2024-881-2024.
    [21]
    LI Zeyi, ZHANG Haitao, and HUANG Yihang. A rotation-invariant optical and SAR image registration algorithm based on deep and Gaussian features[J]. Remote Sensing, 2021, 13(13): 2628. doi: 10.3390/rs13132628.
    [22]
    YU Wei, SUN Xiaohuai, YANG Kuiyuan, et al. Hierarchical semantic image matching using CNN feature pyramid[J]. Computer Vision and Image Understanding, 2018, 169: 40–51. doi: 10.1016/j.cviu.2018.01.001.
    [23]
    SAUVALLE B and DE LA FORTELLE A. Unsupervised multi-object segmentation using attention and soft-argmax[C]. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2023: 3267–3276. doi: 10.1109/WACV56688.2023.00328.
    [24]
    NUNES C F G and PÁDUA F L C. A local feature descriptor based on Log-Gabor filters for keypoint matching in multispectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10): 1850–1854. doi: 10.1109/LGRS.2017.2738632.
    [25]
    HOSANG J, BENENSON R, and SCHIELE B. Learning non-maximum suppression[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 4507–4515. doi: 10.1109/CVPR.2017.685.
    [26]
    CHUNG S W, CHUNG J S, and KANG H G. Perfect match: Self-supervised embeddings for cross-modal retrieval[J]. IEEE Journal of Selected Topics in Signal Processing, 2020, 14(3): 568–576. doi: 10.1109/JSTSP.2020.2987720.
    [27]
    CHEN Feng, WU Fei, XU Jing, et al. Adaptive deformable convolutional network[J]. Neurocomputing, 2021, 453: 853–864. doi: 10.1016/j.neucom.2020.06.128.
    [28]
    KILIÇARSLAN S and CELIK M. RSigELU: A nonlinear activation function for deep neural networks[J]. Expert Systems with Applications, 2021, 174: 114805. doi: 10.1016/j.eswa.2021.114805.
    [29]
    XU Jin, LI Zishan, DU Bowen, et al. Reluplex made more practical: Leaky ReLU[C]. 2020 IEEE Symposium on Computers and Communications, Rennes, France, 2020: 1–7. doi: 10.1109/ISCC50000.2020.9219587.
    [30]
    LI Jiayuan, HU Qingwu, and AI Mingyao. RIFT: Multi-modal image matching based on radiation-variation insensitive feature transform[J]. IEEE Transactions on Image Processing, 2020, 29: 3296–3310. doi: 10.1109/TIP.2019.2959244.
    [31]
    GERMAIN H, BOURMAUD G, and LEPETIT V. S2DNet: Learning image features for accurate sparse-to-dense matching[C]. The 16th European Conference on Computer Vision, Glasgow, UK, 2020: 626–643. doi: 10.1007/978-3-030-58580-8_37.
    [32]
    JAMIN A and HUMEAU-HEURTIER A. (Multiscale) cross-entropy methods: A review[J]. Entropy, 2019, 22(1): 45. doi: 10.3390/e22010045.
    [33]
    YAMADA M, SIGAL L, RAPTIS M, et al. Cross-domain matching with squared-loss mutual information[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1764–1776. doi: 10.1109/TPAMI.2014.2388235.
    [34]
    ZHU Li and ZHU Chunqiang. Application of Hausdorff distance in image matching[C]. 2014 IEEE Workshop on Electronics, Computer and Applications, Ottawa, Canada, 2014: 97–100. doi: 10.1109/IWECA.2014.6845566.
    [35]
    HE Yueping, WANG Xueqian, ZHANG Yiming, et al. A novel loss function for optical and SAR image matching: Balanced positive and negative samples[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4028805. doi: 10.1109/LGRS.2022.3225965.
    [36]
    JIA Weikuan, SUN Meili, LIAN Jian, et al. Feature dimensionality reduction: A review[J]. Complex & Intelligent Systems, 2022, 8(3): 2663–2693. doi: 10.1007/s40747-021-00637-x.
    [37]
    DETONE D, MALISIEWICZ T, and RABINOVICH A. SuperPoint: Self-supervised interest point detection and description[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, USA, 2018: 224–236. doi: 10.1109/CVPRW.2018.00060.
    [38]
    HAN Xufeng, LEUNG T, JIA Yangqing, et al. MatchNet: Unifying feature and metric learning for patch-based matching[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 3279–3286. doi: 10.1109/CVPR.2015.7298948.
    [39]
    HASHIMOTO M, ENOMOTO M, and FUKUSHIMA Y. Coseismic deformation from the 2008 Wenchuan, China, earthquake derived from ALOS/PALSAR images[J]. Tectonophysics, 2010, 491(1/4): 59–71. doi: 10.1016/j.tecto.2009.08.034.
    [40]
    GEUDTNER D, TORRES R, SNOEIJ P, et al. Sentinel-1 system capabilities and applications[C]. 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, Canada, 2014: 1457–1460. doi: 10.1109/IGARSS.2014.6946711.
    [41]
    李志远, 郭嘉逸, 张月婷, 等. 基于自适应动量估计优化器与空变最小熵准则的SAR图像船舶目标自聚焦算法[J]. 雷达学报, 2022, 11(1): 83–94. doi: 10.12000/JR21159.

    LI Zhiyuan, GUO Jiayi, ZHANG Yueting, et al. A novel autofocus algorithm of ship target in SAR image based on the adaptive momentum estimation optimizer and space-variant minimum entropy criteria[J]. Journal of Radars, 2022, 11(1): 83–94. doi: 10.12000/JR21159.
    [42]
    苏娟, 李彬, 王延钊. 一种基于封闭均匀区域的SAR图像配准方法[J]. 电子与信息学报, 2016, 38(12): 3282–3288. doi: 10.11999/JEIT160141.

    SU Juan, LI Bin, and WANG Yanzhao. SAR image registration algorithm based on closed uniform regions[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3282–3288. doi: 10.11999/JEIT160141.
  • Relative Articles

    [1]ZHANG Qiang, WANG Zhihao, WANG Xueqian, LI Gang, HUANG Liwei, SONG Huina, SONG Zhaohui. Cooperative Detection of Ships in Optical and SAR Remote Sensing Images Based on Neighborhood Saliency[J]. Journal of Radars, 2024, 13(4): 885-903. doi: 10.12000/JR24037
    [2]XING Mengdao, MA Penghui, LOU Yishan, SUN Guangcai, LIN Hao. Review of Fast Back Projection Algorithms in Synthetic Aperture Radar[J]. Journal of Radars, 2024, 13(1): 1-22. doi: 10.12000/JR23183
    [3]WANG Yanfei, LI Heping, HAN Song. Synthetic Aperture Imaging of Antenna Array Coded[J]. Journal of Radars, 2023, 12(1): 1-12. doi: 10.12000/JR23011
    [4]XIANG Deliang, XU Yihao, CHENG Jianda, HU Canbin, SUN Xiaokun. An Algorithm Based on a Feature Interaction-based Keypoint Detector and Sim-CSPNet for SAR Image Registration[J]. Journal of Radars, 2022, 11(6): 1081-1097. doi: 10.12000/JR22110
    [5]LIU Fangjian, LI Yuan. SAR Remote Sensing Image Ship Detection Method NanoDet Based on Visual Saliency[J]. Journal of Radars, 2021, 10(6): 885-894. doi: 10.12000/JR21105
    [6]SUN Hao, CHEN Jin, LEI Lin, JI Kefeng, KUANG Gangyao. Adversarial Robustness of Deep Convolutional Neural Network-based Image Recognition Models: A Review[J]. Journal of Radars, 2021, 10(4): 571-594. doi: 10.12000/JR21048
    [7]ZENG Tao, WEN Yuhan, WANG Yan, DING Zegang, WEI Yangkai, YUAN Tiaotiao. Research Progress on Synthetic Aperture Radar Parametric Imaging Methods[J]. Journal of Radars, 2021, 10(3): 327-341. doi: 10.12000/JR21004
    [8]WANG Zhihao, LI Gang, JIANG Xiao. Flooded Area Detection Method Based on Fusion of Optical and SAR Remote Sensing Images[J]. Journal of Radars, 2020, 9(3): 539-553. doi: 10.12000/JR19095
    [9]WEI Yangkai, ZENG Tao, CHEN Xinliang, DING Zegang, FAN Yujie, WEN Yuhan. Parametric SAR Imaging for Typical Lines and Surfaces[J]. Journal of Radars, 2020, 9(1): 143-153. doi: 10.12000/JR19077
    [10]LI Yongzhen, HUANG Datong, XING Shiqi, WANG Xuesong. A Review of Synthetic Aperture Radar Jamming Technique[J]. Journal of Radars, 2020, 9(5): 753-764. doi: 10.12000/JR20087
    [11]HUANG Yan, ZHAO Bo, TAO Mingliang, CHEN Zhanye, HONG Wei. Review of Synthetic Aperture Radar Interference Suppression[J]. Journal of Radars, 2020, 9(1): 86-106. doi: 10.12000/JR19113
    [12]LI Xiaofeng, ZHANG Biao, YANG Xiaofeng. Remote Sensing of Sea Surface Wind and Wave from Spaceborne Synthetic Aperture Radar[J]. Journal of Radars, 2020, 9(3): 425-443. doi: 10.12000/JR20079
    [13]HU Cheng, DENG Yunkai, TIAN Weiming, ZENG Tao. A Compensation Method of Nonlinear Atmospheric Phase Applied for GB-InSAR Images[J]. Journal of Radars, 2019, 8(6): 831-840. doi: 10.12000/JR19073
    [14]XING Mengdao, LIN Hao, CHEN Jianlai, SUN Guangcai, YAN Bangbang. A Review of Imaging Algorithms in Multi-platform-borne Synthetic Aperture Radar[J]. Journal of Radars, 2019, 8(6): 732-757. doi: 10.12000/JR19102
    [15]Ren Xiaozhen, Yang Ruliang. Four-dimensional SAR Imaging Algorithm Based on Iterative Reconstruction of Magnitude and Phase[J]. Journal of Radars, 2016, 5(1): 65-71. doi: 10.12000/JR15135
    [16]Jin Tian. An Enhanced Imaging Method for Foliage Penetration Synthetic Aperture Radar[J]. Journal of Radars, 2015, 4(5): 503-508. doi: 10.12000/JR15114
    [17]Zhang Zhi-long, Yang Wei-ping, Li Ji-cheng. Remote Sensing Image Feature Extracting Based Multiple Ant Colonies Cooperation[J]. Journal of Radars, 2014, 3(1): 92-100. doi: 10.3724/SP.J.1300.2014.13129
    [18]Zeng Cao, Liang Si-jia, Wang Wei, Xu Qing. Imaging Algorithm for Rotor Synthetic Aperture Radar Using Stepped-frequency Waveform[J]. Journal of Radars, 2014, 3(4): 401-408. doi: 10.3724/SP.J.1300.2014.14043
    [19]Chong Jin-song, Zhou Xiao-zhong. Survey of Study on Internal Waves Detection in Synthetic Aperture Radar Image[J]. Journal of Radars, 2013, 2(4): 406-421. doi: 10.3724/SP.J.1300.2013.13012
  • Cited by

    Periodical cited type(4)

    1. 王筝,孙兆阳,李世宝,周航,李凉海,李发政. 快速生成海面目标雷达微多普勒特征的方法. 现代电子技术. 2025(03): 7-12 .
    2. 辛京钰,谷继红,杨婕,丛洲,丁大志. 角反射器阵列排布设计及其散射特性研究. 电波科学学报. 2025(01): 63-71 .
    3. 李铭典,肖顺平,陈思伟. 极化雷达图像目标超分辨率重建研究进展. 电子与信息学报. 2024(05): 1806-1826 .
    4. 阮航,崔家豪,毛秀华,任建迎,罗镔延,曹航,李海峰. SAR目标识别对抗攻击综述:从数字域迈向物理域. 雷达学报. 2024(06): 1298-1326 . 本站查看

    Other cited types(3)

  • Created with Highcharts 5.0.7Amount of accessChart context menuAbstract Views, HTML Views, PDF Downloads StatisticsAbstract ViewsHTML ViewsPDF Downloads2024-052024-062024-072024-082024-092024-102024-112024-122025-012025-022025-032025-040255075100125150
    Created with Highcharts 5.0.7Chart context menuAccess Class DistributionFULLTEXT: 18.9 %FULLTEXT: 18.9 %META: 73.0 %META: 73.0 %PDF: 8.2 %PDF: 8.2 %FULLTEXTMETAPDF
    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 17.1 %其他: 17.1 %其他: 1.8 %其他: 1.8 %Rochester: 0.2 %Rochester: 0.2 %San Mateo: 0.2 %San Mateo: 0.2 %上海: 3.6 %上海: 3.6 %信阳: 0.4 %信阳: 0.4 %兰州: 0.4 %兰州: 0.4 %北京: 16.5 %北京: 16.5 %十堰: 0.7 %十堰: 0.7 %南京: 1.6 %南京: 1.6 %南昌: 0.2 %南昌: 0.2 %南通: 0.2 %南通: 0.2 %台北: 1.3 %台北: 1.3 %台州: 0.5 %台州: 0.5 %合肥: 1.3 %合肥: 1.3 %呼伦贝尔: 0.2 %呼伦贝尔: 0.2 %呼和浩特: 0.2 %呼和浩特: 0.2 %咸阳: 0.2 %咸阳: 0.2 %哈尔滨: 0.2 %哈尔滨: 0.2 %嘉兴: 1.1 %嘉兴: 1.1 %天津: 1.3 %天津: 1.3 %奥卢: 0.7 %奥卢: 0.7 %安康: 0.2 %安康: 0.2 %宜昌: 0.2 %宜昌: 0.2 %宣城: 0.5 %宣城: 0.5 %常州: 0.7 %常州: 0.7 %平顶山: 0.2 %平顶山: 0.2 %广州: 0.5 %广州: 0.5 %库比蒂诺: 0.2 %库比蒂诺: 0.2 %延安: 0.2 %延安: 0.2 %开封: 0.5 %开封: 0.5 %弗吉: 0.2 %弗吉: 0.2 %张家口: 10.0 %张家口: 10.0 %徐州: 0.2 %徐州: 0.2 %怀化: 0.4 %怀化: 0.4 %成都: 0.7 %成都: 0.7 %扬州: 1.3 %扬州: 1.3 %无锡: 0.2 %无锡: 0.2 %昆明: 1.3 %昆明: 1.3 %杭州: 1.1 %杭州: 1.1 %梧州: 0.2 %梧州: 0.2 %武汉: 0.2 %武汉: 0.2 %沈阳: 0.7 %沈阳: 0.7 %济宁: 0.2 %济宁: 0.2 %淄博: 0.2 %淄博: 0.2 %深圳: 0.2 %深圳: 0.2 %温州: 1.1 %温州: 1.1 %湖州: 0.2 %湖州: 0.2 %漯河: 4.5 %漯河: 4.5 %石家庄: 0.2 %石家庄: 0.2 %绍兴: 0.4 %绍兴: 0.4 %芒廷维尤: 10.7 %芒廷维尤: 10.7 %芝加哥: 0.2 %芝加哥: 0.2 %苏州: 0.7 %苏州: 0.7 %衡阳: 0.4 %衡阳: 0.4 %西宁: 2.9 %西宁: 2.9 %西安: 2.7 %西安: 2.7 %诺沃克: 1.5 %诺沃克: 1.5 %邯郸: 0.9 %邯郸: 0.9 %郑州: 1.5 %郑州: 1.5 %重庆: 0.7 %重庆: 0.7 %银川: 0.5 %银川: 0.5 %长沙: 0.5 %长沙: 0.5 %青岛: 0.2 %青岛: 0.2 %鞍山: 0.2 %鞍山: 0.2 %齐齐哈尔: 0.2 %齐齐哈尔: 0.2 %其他其他RochesterSan Mateo上海信阳兰州北京十堰南京南昌南通台北台州合肥呼伦贝尔呼和浩特咸阳哈尔滨嘉兴天津奥卢安康宜昌宣城常州平顶山广州库比蒂诺延安开封弗吉张家口徐州怀化成都扬州无锡昆明杭州梧州武汉沈阳济宁淄博深圳温州湖州漯河石家庄绍兴芒廷维尤芝加哥苏州衡阳西宁西安诺沃克邯郸郑州重庆银川长沙青岛鞍山齐齐哈尔

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(20)  / Tables(4)

    Article views(401) PDF downloads(45) Cited by(7)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    • Table  1.  Parameters for simulations
      Parameter Value Parameter Value
      Element number M 8 d 0.1 m
      Reference frequency f0 1 GHz b 1.4
      {\Delta}f 1 kHz Desired point
      ( \theta_0, r0)
      (0°, 400 km)
       | Show Table
      DownLoad: CSV