传统行人航位推算(Pedestrian Dead Reckoning,PDR)定位技术存在严重的误差累积问题。针对因航向偏差引起的误差累积,提出一种借助建筑几何信息实现行人航向的实时补偿方案,通过提高定向精度来抑制定位误差的累积传递。分析利用外源绝...传统行人航位推算(Pedestrian Dead Reckoning,PDR)定位技术存在严重的误差累积问题。针对因航向偏差引起的误差累积,提出一种借助建筑几何信息实现行人航向的实时补偿方案,通过提高定向精度来抑制定位误差的累积传递。分析利用外源绝对位置改善PDR定位结果,试验一种自适应模型噪声的扩展卡尔曼滤波(Extended Kalman Filter,EKF)滤波算法,实现PDR与WIFI定位源的滤波融合。通过实验对比分析,基于改正航向的PDR相较于传统PDR,有效抑制了误差的累积,将整体误差控制在5 m左右;传统PDR与WIFI源滤波融合,比单纯传统PDR提高了82.8%的精确度;航向改正PDR与WIFI源相融合,则比单纯传统PDR和航向改正PDR分别提高了90.2%和49.5%的精确度。结果表明:补偿改正航向和借助外源绝对位置滤波融合均可有效控制传统PDR的误差累积,根据条件约束可知航向改正PDR及其与WIFI源融合方案较适用于规则室内环境,而原始航向PDR与WIFI源融合方案则不受室内结构影响,在多次滤波后逐渐提高行人定位精度,从而可满足行人室内定位精度需求。展开更多
Dense matching of remote sensing images is a key step in the generation of accurate digital surface models.The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matchin...Dense matching of remote sensing images is a key step in the generation of accurate digital surface models.The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency,so it is widely used in close-range,aerial and satellite image matching.Based on the analysis of the original semi-global matching algorithm,this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on a large amount of high-resolution remote sensing image data and the characteristics of clear image texture.123123The method includes 4 parts:①Precise orientation.Aiming at the system error in the image orientation model,the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images,and the sub-pixel positioning accuracy is obtained;②Epipolar image generation.After the original image is divided into blocks,the epipolar image is generated based on the projection trajectory method;③Image dense matching.In order to reduce the size of the cost space and calculation time,the image is pyramided and then semi-globally matched layer by layer.In the matching process,the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects;④Generate DSM.In order to test the matching effect,the weighted median filter algorithm is used to filter the disparity map,and the DSM is obtained based on the principle of forward intersection.Finally,the paper uses the matching results of WordView3 and Ziyuan No.3 stereo image to verify the effectiveness of this method.展开更多
Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern recognition.It has been widely used and studied in the multi-view clustering t...Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern recognition.It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm.This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different views.Two specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is presented.Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.展开更多
针对多数高空间分辨率的影像波段数量较少,导致改进归一化水体指数(modified normalized difference water index,MNDWI)、新型水体指数(new water index,NWI)和自动水体指数(automated water extraction index,AWEI)等水体指数模型不...针对多数高空间分辨率的影像波段数量较少,导致改进归一化水体指数(modified normalized difference water index,MNDWI)、新型水体指数(new water index,NWI)和自动水体指数(automated water extraction index,AWEI)等水体指数模型不能有效应用的问题,通过分析各地物的光谱信息,构建出新阴影水体指数(new shaded water index,NSWI),旨在提高水体提取精度。在分析现有水体提取方法的基础上提出了新的水体提取方法,采用具有代表性的归一化水体指数(normalized difference water index,NDWI)、支持向量机(support vector machine,SVM)、最大似然(maximum likelihood,ML)法和本文方法,利用不同时期和不同区域的高分辨率影像GF-2以及稳健性分析时选取的中分辨率影像Landsat TM和Landsat OLI进行实验。结果表明:GF-2数据中,本文方法在渭河和玫瑰湖的总体精度分别为97.2%和95.3%,Kappa系数分别为0.966和0.936;在稳健性分析时,采用Landsat TM和Landsat OLI数据,本文方法在东湖和龙羊峡水库的总体精度分别为96.6%和93.4%,均高于其他方法。本文方法充分利用了影像的光谱信息、几何信息和纹理信息,使得提取的地表水体既有较完整的水体边界又保持了局部细节,同时有效抑制了同谱异物、同物异谱现象,减少了噪声斑块的产生,使得提取结果更准确。展开更多
基金National Natural Science Foundation of China(41871367)Ministry of Science and Technology of the People’s Republic of China(2018YFE0206100)。
文摘Dense matching of remote sensing images is a key step in the generation of accurate digital surface models.The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency,so it is widely used in close-range,aerial and satellite image matching.Based on the analysis of the original semi-global matching algorithm,this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on a large amount of high-resolution remote sensing image data and the characteristics of clear image texture.123123The method includes 4 parts:①Precise orientation.Aiming at the system error in the image orientation model,the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images,and the sub-pixel positioning accuracy is obtained;②Epipolar image generation.After the original image is divided into blocks,the epipolar image is generated based on the projection trajectory method;③Image dense matching.In order to reduce the size of the cost space and calculation time,the image is pyramided and then semi-globally matched layer by layer.In the matching process,the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects;④Generate DSM.In order to test the matching effect,the weighted median filter algorithm is used to filter the disparity map,and the DSM is obtained based on the principle of forward intersection.Finally,the paper uses the matching results of WordView3 and Ziyuan No.3 stereo image to verify the effectiveness of this method.
基金This work was supported by the National Key Research and Development Project of China(No.2019YFB2102500)the Strategic Priority CAS Project(No.XDB38040200)+2 种基金the National Natural Science Foundation of China(Nos.62206269,U1913210)the Guangdong Provincial Science and Technology Projects(Nos.2022A1515011217,2022A1515011557)the Shenzhen Science and Technology Projects(No.JSGG20211029095546003)。
文摘Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern recognition.It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm.This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different views.Two specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is presented.Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.
文摘针对多数高空间分辨率的影像波段数量较少,导致改进归一化水体指数(modified normalized difference water index,MNDWI)、新型水体指数(new water index,NWI)和自动水体指数(automated water extraction index,AWEI)等水体指数模型不能有效应用的问题,通过分析各地物的光谱信息,构建出新阴影水体指数(new shaded water index,NSWI),旨在提高水体提取精度。在分析现有水体提取方法的基础上提出了新的水体提取方法,采用具有代表性的归一化水体指数(normalized difference water index,NDWI)、支持向量机(support vector machine,SVM)、最大似然(maximum likelihood,ML)法和本文方法,利用不同时期和不同区域的高分辨率影像GF-2以及稳健性分析时选取的中分辨率影像Landsat TM和Landsat OLI进行实验。结果表明:GF-2数据中,本文方法在渭河和玫瑰湖的总体精度分别为97.2%和95.3%,Kappa系数分别为0.966和0.936;在稳健性分析时,采用Landsat TM和Landsat OLI数据,本文方法在东湖和龙羊峡水库的总体精度分别为96.6%和93.4%,均高于其他方法。本文方法充分利用了影像的光谱信息、几何信息和纹理信息,使得提取的地表水体既有较完整的水体边界又保持了局部细节,同时有效抑制了同谱异物、同物异谱现象,减少了噪声斑块的产生,使得提取结果更准确。
