Rapid Line-Extraction Method for SAR Images Based on Edge-Field Features

This letter proposes a rapid line-extraction (RLE) method for synthetic aperture radar (SAR) images. RLE first transforms an image in the space domain into an image in the frequency domain. Then, using the central-slice theorem, RLE skilfully maps the image in the frequency domain into a parameter s...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2017-10, Vol.14 (10), p.1865-1869
Main Authors: Wei, Qian-Ru, Feng, Da-Zheng, Zheng, Wei, Zheng, Jiang-Bin
Format: Article
Language:English
Subjects:
Clutter
Complexity
Computation
Computational complexity
Computer applications
Detection
Dye dispersion
Edge-field image
Feature extraction
Fourier transforms
Hough transform (HT)
Hough transformation
Image edge detection
Methods
Radar
Radar imaging
SAR (radar)
Sharpness
straight-line detection
Synthetic aperture radar
synthetic aperture radar (SAR) image
Theoretical analysis
Transformations (mathematics)
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Summary:This letter proposes a rapid line-extraction (RLE) method for synthetic aperture radar (SAR) images. RLE first transforms an image in the space domain into an image in the frequency domain. Then, using the central-slice theorem, RLE skilfully maps the image in the frequency domain into a parameter space, which effectively accelerates the straight-line extraction process. Unlike the traditional Hough transform, RLE is performed directly on an edge-field image rather than on a binary edge map. Theoretical analysis proves the advantages of using the edge-field map. Notably, the computational complexity can be greatly reduced relative to the complexity of obtaining a binary edge map, and the method can efficiently avoid the negative influence of false edges in the binary edge map. More importantly, because speckle, clutter, and blurred edges in real-world images decrease the sharpness of peaks, edge-field images that include the strength and direction information of SAR images are adopted to reduce the diffusion of peaks and improve the detection accuracy. Experimental studies show that RLE works independently, is robust to noise, has low computational complexity, achieves high true-positive detection rates, and yields satisfactory detection precision.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2017.2738703