The Microwave Temperature Sounder(MWTS)-2 has a total of 13 temperature-sounding channels with the capability of observing radiance emissions from near the surface to the stratosphere. Similar to the Advanced Technolo...The Microwave Temperature Sounder(MWTS)-2 has a total of 13 temperature-sounding channels with the capability of observing radiance emissions from near the surface to the stratosphere. Similar to the Advanced Technology Microwave Sounder(ATMS), striping pattern noise, primarily in the cross-track direction, exists in MWTS-2 radiance observations. In this study, an algorithm based on principal component analysis(PCA) combined with ensemble empirical mode decomposition(EEMD) is described and applied to MWTS-2 brightness temperature observations. It is arguably necessary to smooth the first three principal component(PC) coefficients by removing the first four intrinsic mode functions(IMFs) using the EEMD method(denoted as PC3/IMF4). After the PC3/IMF4 noise mitigation, the striping pattern noise is effectively removed from the brightness temperature observations. The noise level in MWTS-2 observations is significantly higher than that detected in ATMS observations. In May 2014, the scanning profile of MWTS-2 was adjusted from varying-speed scanning to constantspeed scanning. The impact on striping noise levels brought on by this scan profile change is also analyzed here. The striping noise in brightness temperature observations worsened after the profile change. Regardless of the scan profile change, the striping noise mitigation method reported in this study can more or less suppress the noise levels in MWTS-2 observations.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 the National Key R&D Program (Grant No. 2018YFC1506702)
文摘The Microwave Temperature Sounder(MWTS)-2 has a total of 13 temperature-sounding channels with the capability of observing radiance emissions from near the surface to the stratosphere. Similar to the Advanced Technology Microwave Sounder(ATMS), striping pattern noise, primarily in the cross-track direction, exists in MWTS-2 radiance observations. In this study, an algorithm based on principal component analysis(PCA) combined with ensemble empirical mode decomposition(EEMD) is described and applied to MWTS-2 brightness temperature observations. It is arguably necessary to smooth the first three principal component(PC) coefficients by removing the first four intrinsic mode functions(IMFs) using the EEMD method(denoted as PC3/IMF4). After the PC3/IMF4 noise mitigation, the striping pattern noise is effectively removed from the brightness temperature observations. The noise level in MWTS-2 observations is significantly higher than that detected in ATMS observations. In May 2014, the scanning profile of MWTS-2 was adjusted from varying-speed scanning to constantspeed scanning. The impact on striping noise levels brought on by this scan profile change is also analyzed here. The striping noise in brightness temperature observations worsened after the profile change. Regardless of the scan profile change, the striping noise mitigation method reported in this study can more or less suppress the noise levels in MWTS-2 observations.
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.
