CHEN Siwei, CUI Xingchao, LI Mingdian, et al. SAR image active jamming type recognition based on deep CNN model[J]. Journal of Radars, 2022, 11(5): 897–908. doi: 10.12000/JR22143
Citation:
HUANG Pingping, DUAN Yinghong, TAN Weixian, et al. Change detection method based on fusion difference map in flood disaster[J]. Journal of Radars, 2021, 10(1): 143–158. doi: 10.12000/JR20118
CHEN Siwei, CUI Xingchao, LI Mingdian, et al. SAR image active jamming type recognition based on deep CNN model[J]. Journal of Radars, 2022, 11(5): 897–908. doi: 10.12000/JR22143
Citation:
HUANG Pingping, DUAN Yinghong, TAN Weixian, et al. Change detection method based on fusion difference map in flood disaster[J]. Journal of Radars, 2021, 10(1): 143–158. doi: 10.12000/JR20118
College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China
Funds:
The National Natural Science Foundation of China (61631011), Major Science and Technology Project of Inner Mongolia (2019ZD022), Planned Project of Science and Technology of Inner Mongolia (2019GG139), Innovation Guidance Project of Inner Mongolia (KCBJ2017, KCBJ2018014)
Due to the influence of the environment on the scattering characteristics of ground objects in flooded areas, the false error rate of the detection results increases when performing change detection on Synthetic Aperture Radar (SAR) images of these areas, which reduces the accuracy of the results obtained for the difference map. To solve this problem, in this paper, we propose a change-detection method based on a fusion difference map. This method combines the regional sensitivity of the entropy difference map with the regional retention of the mean difference map to construct a fusion difference map based on an improved relative entropy and mean value ratio. First, the initial clustering results of the fuzzy local information C-means clustering method are classified by their Pearson correlation coefficients, and second, the secondary classification results are used for the initial image segmentation. Third, the final segmentation results are obtained using the iterative condition model and Markov random field. To verify the flood-disaster-detection performance of the proposed method, we used the second of Europe Remote-Sensing (ERS-2) Satellite data obtained for the Bern area in Switzerland in April and May 1999 and Radarsat remote-sensing data for the Ottawa region in Canada in May and August 1997. We also applied the proposed method to data obtained for the Poyang Lake region of China in June and July 2020, and estimated the disaster area and change trend before and after the flood in Poyang Lake. The experimental results show that the algorithm had a low overall detection error, the false error rate of the detection results were somewhat reduced, and the accuracy of the detection results was improved.
表2给出了在不同的目标物体与发射端辐射器距离下,平面波和涡旋微波量子照射隐身目标物体所得到的回波功率(V表示垂直极化,H表示水平极化,V-V表示垂直极化发射和垂直极化接收,其他表示同理)。其中目标物体材质为金属衬底覆盖隐身材料,距离为60 cm, 100 cm 和140 cm,均大于远场区条件24.2 cm。根据测量结果可以看出,对于由隐身吸波材料构成的平板目标,涡旋微波量子与平面波相比,最大回波功率提升8.78 dB。约9 dB的性能提升与理论计算相一致,从而通过实验验证了前述理论的正确性。
表
2
实验中不同距离下平面波和涡旋微波量子的归一化回波功率
Table
2.
Normalized echo power with regards to distance between the antenna and target considering the plane wave and vortex microwave photon in experiment
仿真场景如下:考虑机载雷达场景,工作频段为Ka波段,存在3个隐身目标物体,分别距离雷达收发端50 km, 60 km和75 km,其电参数与第4.2节中给出的3种典型隐身材料一致。采用模态0(即平面波)、模态1和模态2的涡旋微波量子照射目标。假定各个模态的辐射效率和波束形状一致。雷达信号采用线性调频信号填塞脉冲,脉冲压缩比为5,脉冲重复频率保证所有目标物体均落在距离门之内。3种不同模态的信号具有相同的脉冲填塞信号形式和发射功率,即相同中心频率和压缩比的线性调频信号。这些条件保证了目标物体的接收信号功率的差异完全归因于OAM模态的不同。在距离向上,针对每种隐身材料,平面波和涡旋微波量子均在3个目标物位置处(50 km, 60 km和75 km)输出较大功率。由于目标物体均落在距离门内,一维距离成像上不会出现距离模糊。
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CHEN Siwei, CUI Xingchao, LI Mingdian, et al. SAR image active jamming type recognition based on deep CNN model[J]. Journal of Radars, 2022, 11(5): 897–908. doi: 10.12000/JR22143
CHEN Siwei, CUI Xingchao, LI Mingdian, et al. SAR image active jamming type recognition based on deep CNN model[J]. Journal of Radars, 2022, 11(5): 897–908. doi: 10.12000/JR22143
Table
2.
Normalized echo power with regards to distance between the antenna and target considering the plane wave and vortex microwave photon in experiment