KAZE-SAR: SAR image registration using KAZE detector and modified SURF descriptor for tackling speckle noise

نویسندگانMohammadreza Pourfard- Tahmineh Hosseinian- Roghayeh Saeidi- Sayed Ahmad Motamedi- Mohammad Javad Abdollahifard- Reza Mansoori- Reza Safabakhshh
نشریهIEEE Transactions on Geoscience and Remote Sensing
ارائه به نام دانشگاهتفرش
شماره صفحات1-12
شماره مجلد60
ضریب تاثیر (IF)5.6
نوع مقالهFull Paper
تاریخ انتشار18 June 2021
رتبه نشریهISI
نوع نشریهچاپی
کشور محل چاپایالات متحدهٔ امریکا

چکیده مقاله

Synthetic aperture radar (SAR) image registration is still a challenging task in remote sensing. The scale-invariant feature transform (SIFT) method and its extensions are most widely used feature detectors in SAR images. However, due to the presence of nonlinear speckle noise, some wrong features are chosen which directly influence the feature matching process. In this article, for the first time, the KAZE algorithm with a modified version of speeded-up robust features (SURF) descriptor is used for SAR image registration. It uses discrete stochastic second order nonlinear partial differential equations (PDEs) to model the SAR image’s edge structure. KAZE uses nonlinear diffusion filtering to build up the scale levels of the SIFT descriptor. It preserves the edges while simultaneously decreasing the speckle noise in smoothing the image. Therefore, it captures the exact location of features in down-sampling and filtering stages of SIFT. Experimental results show that our proposed method has increased the localization accuracy and distinctiveness of feature extraction through the use of KAZE detector. Our method is also able to handle challenging cases in different image sources, different view angles, different image times, complex affine mapping, presence of blurring, and noise.

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