Based on the project of land macroscopical monitoring by CBERS,a remote sensing image of Arongqi in Inner Mongolia was studied by different methods such as histogram matching,principal component analysis,moment matchi...Based on the project of land macroscopical monitoring by CBERS,a remote sensing image of Arongqi in Inner Mongolia was studied by different methods such as histogram matching,principal component analysis,moment matching,low-pass filter and wavelet transform.A qualitative analysis and quantitative assessment was also carried out.The results showed that wavelet transform could effectively remove stripe noise,and also kept its advantages in the details.Moment matching had a better strip removal,but it changed features in its spectrum easily and it was not fit for CBERS-02 image processing.Principal component analysis could not remove stripe noise,but also strengthened it in a certain extent.展开更多
Airborne LiDAR data are usually collected with partially overlapping strips in order to serve a seamless and fine resolution mapping purpose.One of the factors limiting the use of intensity data is the presence of str...Airborne LiDAR data are usually collected with partially overlapping strips in order to serve a seamless and fine resolution mapping purpose.One of the factors limiting the use of intensity data is the presence of striping noise found in the overlapping region.Though recent researches have proposed physical and empirical approaches for intensity data correction,the effect of striping noise has not yet been resolved.This paper presents a radiometric normalization technique to normalize the intensity data from one data strip to another one with partial overlap.The normalization technique is built based on a second-order polynomial function fitted on the joint histogram plot,which is generated with a set of pairwise closest data points identified within the overlapping region.The proposed method was tested with two individual LiDAR datasets collected by Teledyne Optech’s Gemini(1064 nm)and Orion(1550 nm)sensors.The experimental results showed that radiometric correction and normalization can significantly reduce the striping noise found in the overlapping LiDAR intensity data and improve its capability in land cover classification.The coefficient of variation of five selected land cover features was reduced by 19–65%,where a 9–18%accuracy improvement was achieved in different classification scenarios.With the proven capability of the proposed method,both radiometric correction and normalization should be applied as a pre-processing step before performing any surface classification and object recognition.展开更多
基金Supported by Application and Studies on Land Macroeconomic Monitoring of CBERS from Ministry of Land and Resources
文摘Based on the project of land macroscopical monitoring by CBERS,a remote sensing image of Arongqi in Inner Mongolia was studied by different methods such as histogram matching,principal component analysis,moment matching,low-pass filter and wavelet transform.A qualitative analysis and quantitative assessment was also carried out.The results showed that wavelet transform could effectively remove stripe noise,and also kept its advantages in the details.Moment matching had a better strip removal,but it changed features in its spectrum easily and it was not fit for CBERS-02 image processing.Principal component analysis could not remove stripe noise,but also strengthened it in a certain extent.
基金The research was supported by the Natural Sciences and Engineering Research Council of Canada[RGPIN-2015-03960].
文摘Airborne LiDAR data are usually collected with partially overlapping strips in order to serve a seamless and fine resolution mapping purpose.One of the factors limiting the use of intensity data is the presence of striping noise found in the overlapping region.Though recent researches have proposed physical and empirical approaches for intensity data correction,the effect of striping noise has not yet been resolved.This paper presents a radiometric normalization technique to normalize the intensity data from one data strip to another one with partial overlap.The normalization technique is built based on a second-order polynomial function fitted on the joint histogram plot,which is generated with a set of pairwise closest data points identified within the overlapping region.The proposed method was tested with two individual LiDAR datasets collected by Teledyne Optech’s Gemini(1064 nm)and Orion(1550 nm)sensors.The experimental results showed that radiometric correction and normalization can significantly reduce the striping noise found in the overlapping LiDAR intensity data and improve its capability in land cover classification.The coefficient of variation of five selected land cover features was reduced by 19–65%,where a 9–18%accuracy improvement was achieved in different classification scenarios.With the proven capability of the proposed method,both radiometric correction and normalization should be applied as a pre-processing step before performing any surface classification and object recognition.
