WU Hong'an, TANG Yixian, ZHANG Hong, WANG Chao. Flat-Phase Removal for ALOS/PALSAR Interferometry Based on Orbit State Vectors[J]. Geomatics and Information Science of Wuhan University, 2010, 35(1): 92-96.
Citation:
WU Hong'an, TANG Yixian, ZHANG Hong, WANG Chao. Flat-Phase Removal for ALOS/PALSAR Interferometry Based on Orbit State Vectors[J]. Geomatics and Information Science of Wuhan University, 2010, 35(1): 92-96.
WU Hong'an, TANG Yixian, ZHANG Hong, WANG Chao. Flat-Phase Removal for ALOS/PALSAR Interferometry Based on Orbit State Vectors[J]. Geomatics and Information Science of Wuhan University, 2010, 35(1): 92-96.
Citation:
WU Hong'an, TANG Yixian, ZHANG Hong, WANG Chao. Flat-Phase Removal for ALOS/PALSAR Interferometry Based on Orbit State Vectors[J]. Geomatics and Information Science of Wuhan University, 2010, 35(1): 92-96.
1Center for Earth Observation and Digital Earth,Chinese Academy of Sciences,45 West Beisanhuan Road,Beijing 100086,China;2Chinese Academy of Surveying and Mapping,16 Beitaiping Road,Beijing 100039,China
Flat-phase is the linear phase which is caused by flat ground in the interferometric processing of SAR.It increases the difficulties of phase unwrapping,and its removal accuracy also affects the processing results of InSAR.So flat-phase removal is one of the critical steps of InSAR processing.Generally,flat-phase can be calculated accurately if precise orbit state vectors are available.However,the orbit sampling interval of ALOS satellite is 60-second.Generating the flat phase exactly is one of the difficulties in interferometric processing for ALOS/PALSAR data,which also limits the InSAR application of PALSAR data.In this paper,by analyzing different methods for estimating orbit state vectors,we utilize the Hermite interpolation on equidistant nodes and apply the estimated results to remove flat-phase.ALOS/PALSAR data of Changzhou City,Jiangsu Province were used to evaluate the proposed method.Finally,we compared Hermite interpolation on equidistant nodes with cubic spline interpolation and least squares polynomial regression.
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