The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision agriculture.UAV remote sensing has the advantages of low cost use,simple operation,real-time acquisition of rem...The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision agriculture.UAV remote sensing has the advantages of low cost use,simple operation,real-time acquisition of remote sensor images and high ground resolution.It is difficult to separate cultivated land from other terrain by using only a single feature,making it necessary to extract cultivated land by combining various features and hierarchical classification.In this study,the UAV platform was used to collect visible light remote sensing images of farmland to monitor and extract the area information,shape information and position information of farmland.Based on the vegetation index,texture information and shape information in the visible light band,the object-oriented method was used to study the best scheme for extracting cultivated land area.After repeated experiments,it has been determined that the segmentation scale 50 and the consolidation scale 90 are the most suitable segmentation parameters.Uncultivated crops and other features are separated by using the band information and texture information.The overall accuracy of this method is 86.40%and the Kappa coefficient is 0.80.The experimental results show that the UAV visible light remote sensing data can be used to classify and extract cultivated land with high precision.However,there are some cases where the finely divided plots are misleading,so further optimization and improvement are needed.展开更多
近年来,波段选择在高光谱图像降维处理中得到了广泛地应用,然而常用的数据降维方法并没能将与人类视觉系统相关的信息进行有效利用,如果将人类与生俱来的视觉注意机制能力应用到高光谱图像中目标的视觉显著性特征的增强或识别,对于高光...近年来,波段选择在高光谱图像降维处理中得到了广泛地应用,然而常用的数据降维方法并没能将与人类视觉系统相关的信息进行有效利用,如果将人类与生俱来的视觉注意机制能力应用到高光谱图像中目标的视觉显著性特征的增强或识别,对于高光谱图像的目标检测研究无疑会产生相当的促进作用。研究提出引入视觉注意机制理论应用于波段选择研究,构建面向目标检测应用的视觉注意机制波段选择模型。通过分析计算波段图幅的目标与背景的可识别程度,量化所在波段对地物目标与背景的判别能力,提出了基于目标视觉可识别度的波段选择方法;利用LC显著性算法进行空间域的视觉显著性目标分析,计算背景与目标的显著性差异绝对值,提出基于LC显著目标结构分布的波段选择方法。将这两种方法结合提出的改进子空间划分方法,建立面向目标检测的视觉注意机制波段选择模型,并经高光谱遥感AVIRIS San Diego公开数据集进行目标检测实验验证,结果表明所提出的基于视觉注意机制的波段选择模型对于目标检测应用具有较好的检测效果,实现了数据降维和高效的计算处理。展开更多
A wavelet analysis method is introduced to study the possible periods of PKS 1510-089 in radio bands. By compiling the radio light curve of PKS 1510-089 at frequencies of 22 and 37 GHz from 1990 to 2005, and using the...A wavelet analysis method is introduced to study the possible periods of PKS 1510-089 in radio bands. By compiling the radio light curve of PKS 1510-089 at frequencies of 22 and 37 GHz from 1990 to 2005, and using the wavelet analysis method, the evidence of quasi-periodic activity in PKS 1510-089 was obtained. The results indicate that: (1) There are two stable outburst periods of T1=(1.80±0.06) yr and T2= (0.90±0.07) yr presenting in the isoplethal map of PKS 1510-089 at the radio band 37 GHz; (2) there is an outburst period of T1=(1.80±0.06) yr presenting at the radio band 22 GHz; (3) by the continuum of the isograms map, we find the primary period of PKS 1510-089 is T1=(1.80±0.06) yr, and the period of T2= (0.90±0.07) yr may be the half period. The above results are consistent with the reports of Xie et al. (2004, 2005, 2008), Wu et al. (2005) and Liu & Fan (2007), using the other methods. We can expect that the next burst will be in January 2011.展开更多
We analyze the radio light curve of 3C 273 at 15 GHz from 1963 to 2006 taken from the database of the literature,and find evidence of quasi-periodic activity.Using the wavelet analysis method to analyze these data,our...We analyze the radio light curve of 3C 273 at 15 GHz from 1963 to 2006 taken from the database of the literature,and find evidence of quasi-periodic activity.Using the wavelet analysis method to analyze these data,our results indicate that:(1) There is one main outburst period of P1=8.1±0.1 year in 3C 273.This period is in a good agreement with Ozernoi's analysis in optical bands.(2) Based on the possible periods,we expect the next burst in 2014 October.展开更多
基金We acknowledge that this research work was financially supported by the Leading Talents of Guangdong Province Program(Project No.2016LJ06G689)Educational Commission of Guangdong Province of China for Platform(Project No.2015KGJHZ007)+1 种基金Science and Technology Planning Project of Guangdong Province(Project No.2017B010117010)China Agriculture Research System(Project No.CARS-15-22)。
文摘The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision agriculture.UAV remote sensing has the advantages of low cost use,simple operation,real-time acquisition of remote sensor images and high ground resolution.It is difficult to separate cultivated land from other terrain by using only a single feature,making it necessary to extract cultivated land by combining various features and hierarchical classification.In this study,the UAV platform was used to collect visible light remote sensing images of farmland to monitor and extract the area information,shape information and position information of farmland.Based on the vegetation index,texture information and shape information in the visible light band,the object-oriented method was used to study the best scheme for extracting cultivated land area.After repeated experiments,it has been determined that the segmentation scale 50 and the consolidation scale 90 are the most suitable segmentation parameters.Uncultivated crops and other features are separated by using the band information and texture information.The overall accuracy of this method is 86.40%and the Kappa coefficient is 0.80.The experimental results show that the UAV visible light remote sensing data can be used to classify and extract cultivated land with high precision.However,there are some cases where the finely divided plots are misleading,so further optimization and improvement are needed.
文摘近年来,波段选择在高光谱图像降维处理中得到了广泛地应用,然而常用的数据降维方法并没能将与人类视觉系统相关的信息进行有效利用,如果将人类与生俱来的视觉注意机制能力应用到高光谱图像中目标的视觉显著性特征的增强或识别,对于高光谱图像的目标检测研究无疑会产生相当的促进作用。研究提出引入视觉注意机制理论应用于波段选择研究,构建面向目标检测应用的视觉注意机制波段选择模型。通过分析计算波段图幅的目标与背景的可识别程度,量化所在波段对地物目标与背景的判别能力,提出了基于目标视觉可识别度的波段选择方法;利用LC显著性算法进行空间域的视觉显著性目标分析,计算背景与目标的显著性差异绝对值,提出基于LC显著目标结构分布的波段选择方法。将这两种方法结合提出的改进子空间划分方法,建立面向目标检测的视觉注意机制波段选择模型,并经高光谱遥感AVIRIS San Diego公开数据集进行目标检测实验验证,结果表明所提出的基于视觉注意机制的波段选择模型对于目标检测应用具有较好的检测效果,实现了数据降维和高效的计算处理。
基金Supported by the National Natural Science Foundation of China (Grant Nos. 10821061, 10763002, and 10663002)the National Basic Research Program of China (Grant No. 2007CB815103)the Natural Science Foundation of Yunnan Province (Grant No. 2008CC011)
文摘A wavelet analysis method is introduced to study the possible periods of PKS 1510-089 in radio bands. By compiling the radio light curve of PKS 1510-089 at frequencies of 22 and 37 GHz from 1990 to 2005, and using the wavelet analysis method, the evidence of quasi-periodic activity in PKS 1510-089 was obtained. The results indicate that: (1) There are two stable outburst periods of T1=(1.80±0.06) yr and T2= (0.90±0.07) yr presenting in the isoplethal map of PKS 1510-089 at the radio band 37 GHz; (2) there is an outburst period of T1=(1.80±0.06) yr presenting at the radio band 22 GHz; (3) by the continuum of the isograms map, we find the primary period of PKS 1510-089 is T1=(1.80±0.06) yr, and the period of T2= (0.90±0.07) yr may be the half period. The above results are consistent with the reports of Xie et al. (2004, 2005, 2008), Wu et al. (2005) and Liu & Fan (2007), using the other methods. We can expect that the next burst will be in January 2011.
基金supported by the National Natural Science Foundation of China (Grant Nos. 10821061,10763002,and 10663002)the National Basic Research Program of China (Grant No. 2009CB824800)
文摘We analyze the radio light curve of 3C 273 at 15 GHz from 1963 to 2006 taken from the database of the literature,and find evidence of quasi-periodic activity.Using the wavelet analysis method to analyze these data,our results indicate that:(1) There is one main outburst period of P1=8.1±0.1 year in 3C 273.This period is in a good agreement with Ozernoi's analysis in optical bands.(2) Based on the possible periods,we expect the next burst in 2014 October.
