研究目的
To develop a robust and permanent change detection algorithm for urban environments through the use of multitemporal and polarimetric SAR data, reducing false detection rates due to seasonal variations and mobile features.
研究成果
The study concluded that the Shannon entropy and the determinant of the polarimetric covariance matrix are the most robust parameters for detecting changes in urban environments. Fitting a hyperbolic tangent model function to the multitemporal polarimetric parameters significantly reduces nonessential changes and provides information on whether buildings were constructed or destroyed and the date of change occurrence.
研究不足
The technique falsely detects some parking lots as changed areas due to the presence or absence of cars from scene to scene. It is also difficult to know when the changes occurred without fitting the hyperbolic tangent model function.
1：Experimental Design and Method Selection:
The study involved determining the most robust polarimetric parameters for change detection in urban environments and applying change detection techniques using a maximum likelihood ratio and a hyperbolic tangent model function to these parameters.
2：Sample Selection and Data Sources:
The data used were acquired by the UAVSAR system over the city of Pasadena, CA, USA, from 2009 to
3：List of Experimental Equipment and Materials:
20 The UAVSAR system, an L-band airborne polarimetric repeat-pass interferometric radar system, was used.
4：Experimental Procedures and Operational Workflow:
The study involved coregistration of multitemporal SAR images, extraction of polarimetric parameters, and application of change detection models.
5：Data Analysis Methods:
The study employed statistical techniques for evaluating the separation between changed and unchanged areas and for fitting the hyperbolic tangent model function to the multitemporal polarimetric parameters.
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