The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate(CFAR) target detection method. G0 distribution is one of the optimal statistic models in the syn...The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate(CFAR) target detection method. G0 distribution is one of the optimal statistic models in the synthetic aperture radar(SAR) image background clutter modeling and can accurately model various complex background clutters in the SAR images. But the application of the distribution is greatly limited by its disadvantages that the parameter estimation is complex and the local detection threshold is difficult to be obtained. In order to solve the above-mentioned problems, an synthetic aperture radar CFAR target detection method using the logarithmic cumulant(Mo LC) + method of moment(Mo M)-based G0 distribution clutter model is proposed. In the method, G0 distribution is used for modeling the background clutters, a new Mo LC+Mo M-based parameter estimation method coupled with a fast iterative algorithm is used for estimating the parameters of G0 distribution and an exquisite dichotomy method is used for obtaining the local detection threshold of CFAR detection, which greatly improves the computational efficiency, detection performance and environmental adaptability of CFAR detection. Experimental results show that the proposed SAR CFAR target detection method has good target detection performance in various complex background clutter environments.展开更多
This paper presents a detailed analysis of the effects of noise (reverberation) on the focusing performance of de-composition of the time reversal operator (DORT) in a noise-limited case and in a reverberation-limited...This paper presents a detailed analysis of the effects of noise (reverberation) on the focusing performance of de-composition of the time reversal operator (DORT) in a noise-limited case and in a reverberation-limited case, respectively. Quantitative results obtained from simulations and experiments are presented. The results show the DORT method can be effi-ciently applied to target detection with enough source level to yield significant backscatter. For a target placed on the bottom, the influence of the reverberation on the focusing performance is slight. However, distinguishing between a target and constant backscattering returning from strong local clutter on the bottom (false alarms) needs further research.展开更多
文摘低表面亮度星系(Low Surface Brightness Galaxy,LSBG)的特征对于理解星系整体特征非常重要,通过现代的机器学习特别是深度学习算法来搜寻扩充低表面亮度星系样本具有重要意义.LSBG因特征不明显而难以用传统方法进行自动和准确辨别,但深度学习确具有自动找出复杂且有效特征的优势,针对此问题提出了一种可用于在大样本巡天观测项目中搜寻LSBG的算法---YOLOX-CS(You Only Look Once version X-CS).首先通过实验对比5种经典目标检测算法并选择较优的YOLOX算法作为基础算法,然后结合不同注意力机制和不同优化器,构建了YOLOX-CS的框架结构.数据集使用的是斯隆数字化巡天(Sloan Digital Sky Survey,SDSS)中的图像,其标签来自于α.40-SDSS DR7(40%中性氢苜蓿巡天与第7次数据发布的斯隆数字化巡天的交叉覆盖天区)巡天项目中的LSBG,由于该数据集样本较少,还采用了深度卷积生成对抗网络(Deep Convolutional Generative Adversarial Networks,DCGAN)模型扩充了实验测试数据.通过与一系列目标检测算法对比后,YOLOX-CS在扩充前后两个数据集中搜索LSBG的召回率和AP(Average Precision)值都有较好的测试结果,其在未扩充数据集的测试集中的召回率达到97.75%,AP值达到97.83%,在DCGAN模型扩充的数据集中,同样测试集下进行实验的召回率达到99.10%,AP值达到98.94%,验证了该算法在LSBG搜索中具有优秀的性能.最后,将该算法应用到SDSS部分测光数据上,搜寻得到了765个LSBG候选体.
基金Project(61105020)supported by the National Natural Science Foundation of ChinaProject(13zxtk08)supported by the Key Research Platform for Research Projects of Southwest University of Science and Technology,China
文摘The accuracy of background clutter model is a key factor which determines the performance of a constant false alarm rate(CFAR) target detection method. G0 distribution is one of the optimal statistic models in the synthetic aperture radar(SAR) image background clutter modeling and can accurately model various complex background clutters in the SAR images. But the application of the distribution is greatly limited by its disadvantages that the parameter estimation is complex and the local detection threshold is difficult to be obtained. In order to solve the above-mentioned problems, an synthetic aperture radar CFAR target detection method using the logarithmic cumulant(Mo LC) + method of moment(Mo M)-based G0 distribution clutter model is proposed. In the method, G0 distribution is used for modeling the background clutters, a new Mo LC+Mo M-based parameter estimation method coupled with a fast iterative algorithm is used for estimating the parameters of G0 distribution and an exquisite dichotomy method is used for obtaining the local detection threshold of CFAR detection, which greatly improves the computational efficiency, detection performance and environmental adaptability of CFAR detection. Experimental results show that the proposed SAR CFAR target detection method has good target detection performance in various complex background clutter environments.
基金Project supported by the National Natural Science Foundation of China (Nos. 60702022 and 60772094)the National Basic Re-search Program (973) of China (No. 5132103ZZT21B)
文摘This paper presents a detailed analysis of the effects of noise (reverberation) on the focusing performance of de-composition of the time reversal operator (DORT) in a noise-limited case and in a reverberation-limited case, respectively. Quantitative results obtained from simulations and experiments are presented. The results show the DORT method can be effi-ciently applied to target detection with enough source level to yield significant backscatter. For a target placed on the bottom, the influence of the reverberation on the focusing performance is slight. However, distinguishing between a target and constant backscattering returning from strong local clutter on the bottom (false alarms) needs further research.
