A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
WSEAS Transactions on Computers
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Synthetic aperture radar (SAR) is an active microwave remote sensing imaging radar, which can obtain abundant electromagnetic information from ground objects, especially in some regions where optics and infrared remote sensing do not work well. It has been widely applied in civil and military fields. But there are some uncertain factors in SAR imaging which not only affect SAR imaging, but also obstruct interpretation and applications of SAR images. In order to restrain and improve these uncertain factors, this paper deeply analyzes and discusses the uncertainties of spaceborne SAR imaging system from the SAR imaging mechanism and imaging process, and proposes some corresponding improved algorithms. These uncertainties, which are caused by Doppler parameter estimation, range migration, speckle noise and object azimuth angle, are explained in detail. The raw data of ERS-2 are used to test these methods, and the experimental results prove that it is efficient for them to improve and control the uncertain factors, and there will be some referential value for further studying SAR imaging and applications.