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CFAR based morphological filter design to remove clutter from GB‐SAR images: An application to real data
Author(s) -
Toktas Abdurrahim,
Yigit Enes,
Sabanci Kadir,
Kayabasi Ahmet
Publication year - 2017
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.30803
Subject(s) - clutter , constant false alarm rate , computer science , filter (signal processing) , synthetic aperture radar , artificial intelligence , computer vision , detector , remote sensing , radar , geology , telecommunications
Abstract Reconstructed ground based synthetic aperture radar (GB‐SAR) data generally presents high resolution images of objects on the ground. However ground and multipath reflections occurred from depression angle appear as a clutter signs in the image. Constant false alarm rate (CFAR) detector is commonly preferred threshold algorithm for removing the clutter. But there is challenge in tuning CFAR. By enlarging or shrinking the window size, the background data taken into account respectively increases or decreases, and thus the filtered image accordingly includes much clutter or less clutter with target information loss. In this study, a novel adaptive clutter removing procedure based on morphological filter (MF) is designed to compensate this tradeoff by applying after a large windowed detector. In order to test the proposed CFAR based MF (CFAR‐MF) method, two different examples of GB‐SAR experiments in which the images are reconstructed by back projection algorithm (BPA) were performed using stepped frequency continuous waveform operating. The performance of CFAR‐MF was then examined in terms of signal to clutter ratio and integrated side lobe ratio. The results show that the clutter remains of the CFAR detector are effectively cleared from the GB‐SAR image and targets were accurately detected at their true locations without any disorder.