After the first Earth Orientation Parameters Prediction Comparison Campaign(1 st EOP PCC),the traditional method using least-squares extrapolation and autoregressive(LS+AR)models was considered as one of the polar mot...After the first Earth Orientation Parameters Prediction Comparison Campaign(1 st EOP PCC),the traditional method using least-squares extrapolation and autoregressive(LS+AR)models was considered as one of the polar motion prediction methods with higher accuracy.The traditional method predicts individual polar motion series separately,which has a single input data and limited improvement in prediction accuracy.To address this problem,this paper proposes a new method for predicting polar motion by combining the difference between polar motion series.The X,Y,and Y-X series were predicted separately using LS+AR models.Then,the new forecast value of X series is obtained by combining the forecast value of Y series with that of Y-X series;the new forecast value of Y series is obtained by combining the forecast value of X series with that of Y-X series.The hindcast experimental comparison results from January 1,2011 to April 4,2021 show that the new method achieves a maximum improvement of 12.95%and 14.96%over the traditional method in the X and Y directions,respectively.The new method has obvious advantages compared with the differential method.This study tests the stability and superiority of the new method and provides a new idea for the research of polar motion prediction.展开更多
为了进一步提高红外小目标的检测性能,针对图像序列中背景与小目标的特点,提出了一种基于非下采样Contourlet变换(nonsubsampled contourlet transform,NSCT)和核模糊C均值(kernel fuzzy C means,KFCM)聚类多模型最小二乘支持向量机(lea...为了进一步提高红外小目标的检测性能,针对图像序列中背景与小目标的特点,提出了一种基于非下采样Contourlet变换(nonsubsampled contourlet transform,NSCT)和核模糊C均值(kernel fuzzy C means,KFCM)聚类多模型最小二乘支持向量机(least squares support vector machine,LS-SVM)背景预测的检测方法。首先对红外小目标图像进行NSCT并去噪,提高图像的信噪比;然后通过基于核模糊C均值聚类的多模型LS-SVM预测去噪后红外图像中的背景,用去噪后的实际图像减去背景预测图像得到残差图像;接着提出基于递归最大类间绝对差的阈值选取算法分割残差图像;最后利用目标灰度的平稳性和运动轨迹的连续性进一步检测出真实的小目标。给出了实验结果与分析,并与现有的3种基于背景预测的小目标检测方法进行了比较。结果表明该方法具有更高的检测概率和信噪比增益。展开更多
基金funded by the National Natural Science Foundation of China(Nos.42174011 and 41874001)Jiangxi Province Graduate Student Innovation Fund(No.YC2021-S614)+2 种基金Jiangxi Provincial Natural Science Foundation(No.20202BABL212015)the East China University of Technology Ph.D.Project(No.DNBK2019181)the Key Laboratory for Digital Land and Resources of Jiangxi Province,East China University of Technology(No.DLLJ202109)
文摘After the first Earth Orientation Parameters Prediction Comparison Campaign(1 st EOP PCC),the traditional method using least-squares extrapolation and autoregressive(LS+AR)models was considered as one of the polar motion prediction methods with higher accuracy.The traditional method predicts individual polar motion series separately,which has a single input data and limited improvement in prediction accuracy.To address this problem,this paper proposes a new method for predicting polar motion by combining the difference between polar motion series.The X,Y,and Y-X series were predicted separately using LS+AR models.Then,the new forecast value of X series is obtained by combining the forecast value of Y series with that of Y-X series;the new forecast value of Y series is obtained by combining the forecast value of X series with that of Y-X series.The hindcast experimental comparison results from January 1,2011 to April 4,2021 show that the new method achieves a maximum improvement of 12.95%and 14.96%over the traditional method in the X and Y directions,respectively.The new method has obvious advantages compared with the differential method.This study tests the stability and superiority of the new method and provides a new idea for the research of polar motion prediction.
文摘为了进一步提高红外小目标的检测性能,针对图像序列中背景与小目标的特点,提出了一种基于非下采样Contourlet变换(nonsubsampled contourlet transform,NSCT)和核模糊C均值(kernel fuzzy C means,KFCM)聚类多模型最小二乘支持向量机(least squares support vector machine,LS-SVM)背景预测的检测方法。首先对红外小目标图像进行NSCT并去噪,提高图像的信噪比;然后通过基于核模糊C均值聚类的多模型LS-SVM预测去噪后红外图像中的背景,用去噪后的实际图像减去背景预测图像得到残差图像;接着提出基于递归最大类间绝对差的阈值选取算法分割残差图像;最后利用目标灰度的平稳性和运动轨迹的连续性进一步检测出真实的小目标。给出了实验结果与分析,并与现有的3种基于背景预测的小目标检测方法进行了比较。结果表明该方法具有更高的检测概率和信噪比增益。
