目的受遮挡与累积误差因素的影响,现有目标6维(6 dimensions,6D)姿态实时追踪方法在复杂场景中表现不佳。为此,提出了一种高鲁棒性的刚体目标6D姿态实时追踪网络。方法在网络的整体设计上,将当前帧彩色图像和深度图像(red green blue-de...目的受遮挡与累积误差因素的影响,现有目标6维(6 dimensions,6D)姿态实时追踪方法在复杂场景中表现不佳。为此,提出了一种高鲁棒性的刚体目标6D姿态实时追踪网络。方法在网络的整体设计上,将当前帧彩色图像和深度图像(red green blue-depth map,RGB-D)与前一帧姿态估计结果经升维残差采样滤波和特征编码处理获得姿态差异,与前一帧姿态估计结果共同计算目标当前的6D姿态;在残差采样滤波模块的设计中,采用自门控swish(searching for activation functions)激活函数保留目标细节特征,提高目标姿态追踪的准确性;在特征聚合模块的设计中,将提取的特征分解为水平与垂直两个方向分量,分别从时间和空间上捕获长程依赖并保留位置信息,生成一组具有位置与时间感知的互补特征图,加强目标特征提取能力,从而加速网络收敛。结果实验选用YCBVideo(Yale-CMU-Berkeley-video)和YCBInEoAT(Yale-CMU-Berkeley in end-of-arm-tooling)数据集。实验结果表明,本文方法追踪速度达到90.9 Hz,追踪精度模型点平均距离(average distance of model points,ADD)和最近点的平均距离(average closest point distance,ADD-S)分别达到93.24及95.84,均高于同类相关方法。本文方法的追踪精度指标ADD和ADD-S在追踪精度和追踪速度上均领先于目前其他的刚体姿态追踪方法,与se(3)-TrackNet网络相比,本文方法在6000组少量合成数据训练的条件下分别高出25.95和30.91,在8000组少量合成数据训练的条件下分别高出31.72和28.75,在10000组少量合成数据训练的条件下分别高出35.57和21.07,且在严重遮挡场景下能够实现对目标的高鲁棒6D姿态追踪。结论本文网络在合成数据驱动条件下,可以更好地完成实时准确追踪目标6D姿态,网络收敛速度快,实验结果验证了本文方法的有效性。展开更多
A novel algorithm for bridge recognition of median synthetic aperture radar （SAR） images using histogram entropy presented by Pun is proposed. Firstly, Lee filter and histogram proportion are used to denoise the ori...A novel algorithm for bridge recognition of median synthetic aperture radar （SAR） images using histogram entropy presented by Pun is proposed. Firstly, Lee filter and histogram proportion are used to denoise the original image and to make the target evident. Then, water regions are gained through histogram segmentation and the contours of water regions axe extracted. After these, the potential bridge targets are obtained based on the space relativity between bridges and water regions using improved contour search. At last, bridges are recognized by extracting the feature of Pun histogram entropy （PHE） of these potential bridge targets. Experimental results show the good qualities of the algorithm, such as fast speed, high rate of recognition, and low rate of false target.展开更多
开放骨架磷酸铝化合物是多孔晶体材料的一个重要家族。然而,这类材料的合成受到反应原料、凝胶组成、溶剂、模板剂、结晶温度和结晶时间等多个因素的影响。本文以吉林大学"无机制备与合成化学国家重点实验室"建立的开放骨架...开放骨架磷酸铝化合物是多孔晶体材料的一个重要家族。然而,这类材料的合成受到反应原料、凝胶组成、溶剂、模板剂、结晶温度和结晶时间等多个因素的影响。本文以吉林大学"无机制备与合成化学国家重点实验室"建立的开放骨架磷酸铝合成反应数据库为研究对象,采用最大权重最小冗余特征选择算法(Maximum weight and minimum redundancy,MWMR),在充分考虑合成参数自身的重要程度和合成参数之间的相关关系的前提下,分析了溶剂、模板剂等合成参数对于合成含有(8,6)元环结构开放骨架磷酸铝的影响。通过大量实验验证了该方法在开放骨架磷酸铝合成参数分析中的有效性,分析了合成参数对产物生成的影响。实验结果表明模板剂的几何参数、模板剂中C原子和N原子的个数比,溶剂的偶极距等参数可能对于该类结构的合成具有较为重要的影响。展开更多
Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of vis...Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.展开更多
Synthetic aperture radar(SAR) automatic target recognition is an important application in SAR.How to extract features has restricted the application of SAR technology seriously.In this paper,a new feature extraction m...Synthetic aperture radar(SAR) automatic target recognition is an important application in SAR.How to extract features has restricted the application of SAR technology seriously.In this paper,a new feature extraction method for SAR automatic target recognition based on maximum interclass distance is proposed,which integrates class and neighborhood information.This method can reinforce discriminative power using maximum interclass distance,so it can improve recognition rate effectively.展开更多
This letter studies on the detection of texture features in Synthetic Aperture Radar (SAR) images. Through analyzing the feature detection method proposed by Lopes, an improved texture detection method is proposed, wh...This letter studies on the detection of texture features in Synthetic Aperture Radar (SAR) images. Through analyzing the feature detection method proposed by Lopes, an improved texture detection method is proposed, which can not only detect the edge and lines but also avoid stretching edge and suppressing lines of the former algorithm. Experimental results with both simulated and real SAR images verify the advantage and practicability of the improved method.展开更多
Remote sensing is an important technical means to investigate land resources.Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves,whereas ...Remote sensing is an important technical means to investigate land resources.Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves,whereas synthetic aperture radar(SAR)imagery is sensitive to changes in growth states and morphological structures.Crop-type mapping with a single type of imagery sometimes has unsatisfactory precision,so providing precise spatiotemporal information on crop type at a local scale for agricultural applications is difficult.To explore the abilities of combining optical and SAR images and to solve the problem of inaccurate spatial information for land parcels,a new method is proposed in this paper to improve crop-type identification accuracy.Multifeatures were derived from the full polarimetric SAR data(GaoFen-3)and a high-resolution optical image(GaoFen-2),and the farmland parcels used as the basic for object-oriented classification were obtained from the GaoFen-2 image using optimal scale segmentation.A novel feature subset selection method based on within-class aggregation and between-class scatter(WA-BS)is proposed to extract the optimal feature subset.Finally,crop-type mapping was produced by a support vector machine(SVM)classifier.The results showed that the proposed method achieved good classification results with an overall accuracy of 89.50%,which is better than the crop classification results derived from SAR-based segmentation.Compared with the ReliefF,mRMR and LeastC feature selection algorithms,the WA-BS algorithm can effectively remove redundant features that are strongly correlated and obtain a high classification accuracy via the obtained optimal feature subset.This study shows that the accuracy of crop-type mapping in an area with multiple cropping patterns can be improved by the combination of optical and SAR remote sensing images.展开更多
文摘目的受遮挡与累积误差因素的影响,现有目标6维(6 dimensions,6D)姿态实时追踪方法在复杂场景中表现不佳。为此,提出了一种高鲁棒性的刚体目标6D姿态实时追踪网络。方法在网络的整体设计上,将当前帧彩色图像和深度图像(red green blue-depth map,RGB-D)与前一帧姿态估计结果经升维残差采样滤波和特征编码处理获得姿态差异,与前一帧姿态估计结果共同计算目标当前的6D姿态;在残差采样滤波模块的设计中,采用自门控swish(searching for activation functions)激活函数保留目标细节特征,提高目标姿态追踪的准确性;在特征聚合模块的设计中,将提取的特征分解为水平与垂直两个方向分量,分别从时间和空间上捕获长程依赖并保留位置信息,生成一组具有位置与时间感知的互补特征图,加强目标特征提取能力,从而加速网络收敛。结果实验选用YCBVideo(Yale-CMU-Berkeley-video)和YCBInEoAT(Yale-CMU-Berkeley in end-of-arm-tooling)数据集。实验结果表明,本文方法追踪速度达到90.9 Hz,追踪精度模型点平均距离(average distance of model points,ADD)和最近点的平均距离(average closest point distance,ADD-S)分别达到93.24及95.84,均高于同类相关方法。本文方法的追踪精度指标ADD和ADD-S在追踪精度和追踪速度上均领先于目前其他的刚体姿态追踪方法,与se(3)-TrackNet网络相比,本文方法在6000组少量合成数据训练的条件下分别高出25.95和30.91,在8000组少量合成数据训练的条件下分别高出31.72和28.75,在10000组少量合成数据训练的条件下分别高出35.57和21.07,且在严重遮挡场景下能够实现对目标的高鲁棒6D姿态追踪。结论本文网络在合成数据驱动条件下,可以更好地完成实时准确追踪目标6D姿态,网络收敛速度快,实验结果验证了本文方法的有效性。
文摘A novel algorithm for bridge recognition of median synthetic aperture radar （SAR） images using histogram entropy presented by Pun is proposed. Firstly, Lee filter and histogram proportion are used to denoise the original image and to make the target evident. Then, water regions are gained through histogram segmentation and the contours of water regions axe extracted. After these, the potential bridge targets are obtained based on the space relativity between bridges and water regions using improved contour search. At last, bridges are recognized by extracting the feature of Pun histogram entropy （PHE） of these potential bridge targets. Experimental results show the good qualities of the algorithm, such as fast speed, high rate of recognition, and low rate of false target.
文摘开放骨架磷酸铝化合物是多孔晶体材料的一个重要家族。然而,这类材料的合成受到反应原料、凝胶组成、溶剂、模板剂、结晶温度和结晶时间等多个因素的影响。本文以吉林大学"无机制备与合成化学国家重点实验室"建立的开放骨架磷酸铝合成反应数据库为研究对象,采用最大权重最小冗余特征选择算法(Maximum weight and minimum redundancy,MWMR),在充分考虑合成参数自身的重要程度和合成参数之间的相关关系的前提下,分析了溶剂、模板剂等合成参数对于合成含有(8,6)元环结构开放骨架磷酸铝的影响。通过大量实验验证了该方法在开放骨架磷酸铝合成参数分析中的有效性,分析了合成参数对产物生成的影响。实验结果表明模板剂的几何参数、模板剂中C原子和N原子的个数比,溶剂的偶极距等参数可能对于该类结构的合成具有较为重要的影响。
基金Supported by the National Natural Science Foundation of China (No. 61032001, No.61002045)
文摘Designing detection algorithms with high efficiency for Synthetic Aperture Radar(SAR) imagery is essential for the operator SAR Automatic Target Recognition(ATR) system.This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms,and introduces the gridding and fusion idea of different texture fea-tures to realize fast target detection.It first grids the original SAR imagery,yielding a set of grids to be classified into clutter grids and target grids,and then calculates the texture features in each grid.By fusing the calculation results,the target grids containing potential maneuvering targets are determined.The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest.The fused texture features,including local statistics features and Gray-Level Co-occurrence Matrix(GLCM),are investigated.The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast de-tection algorithms using real SAR data.The results obtained from the experiments indicate the promising practical application val-ue of our study.
基金supported in part by the National High-tech Research and Development Program("863"Program)of China(Grant No.2009AA12Z106)
文摘Synthetic aperture radar(SAR) automatic target recognition is an important application in SAR.How to extract features has restricted the application of SAR technology seriously.In this paper,a new feature extraction method for SAR automatic target recognition based on maximum interclass distance is proposed,which integrates class and neighborhood information.This method can reinforce discriminative power using maximum interclass distance,so it can improve recognition rate effectively.
基金Supported by the University Doctorate Special Research Fund(No.20030614001)
文摘This letter studies on the detection of texture features in Synthetic Aperture Radar (SAR) images. Through analyzing the feature detection method proposed by Lopes, an improved texture detection method is proposed, which can not only detect the edge and lines but also avoid stretching edge and suppressing lines of the former algorithm. Experimental results with both simulated and real SAR images verify the advantage and practicability of the improved method.
基金The authors acknowledge that this study was financially supported by the National Key R&D Programs of China(No.2017YFB0504201)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA20020101)+1 种基金and the Natural Science Foundation of China(No.61473286 and No.61375002)Our sincere thanks go to the students at the State Key Laboratory of Remote Sensing Science for their assistance during the field survey campaigns.
文摘Remote sensing is an important technical means to investigate land resources.Optical imagery has been widely used in crop classification and can show changes in moisture and chlorophyll content in crop leaves,whereas synthetic aperture radar(SAR)imagery is sensitive to changes in growth states and morphological structures.Crop-type mapping with a single type of imagery sometimes has unsatisfactory precision,so providing precise spatiotemporal information on crop type at a local scale for agricultural applications is difficult.To explore the abilities of combining optical and SAR images and to solve the problem of inaccurate spatial information for land parcels,a new method is proposed in this paper to improve crop-type identification accuracy.Multifeatures were derived from the full polarimetric SAR data(GaoFen-3)and a high-resolution optical image(GaoFen-2),and the farmland parcels used as the basic for object-oriented classification were obtained from the GaoFen-2 image using optimal scale segmentation.A novel feature subset selection method based on within-class aggregation and between-class scatter(WA-BS)is proposed to extract the optimal feature subset.Finally,crop-type mapping was produced by a support vector machine(SVM)classifier.The results showed that the proposed method achieved good classification results with an overall accuracy of 89.50%,which is better than the crop classification results derived from SAR-based segmentation.Compared with the ReliefF,mRMR and LeastC feature selection algorithms,the WA-BS algorithm can effectively remove redundant features that are strongly correlated and obtain a high classification accuracy via the obtained optimal feature subset.This study shows that the accuracy of crop-type mapping in an area with multiple cropping patterns can be improved by the combination of optical and SAR remote sensing images.
