Synthetic aperture radar target detection method based on curvelet transformation and Wiener filtering
A synthetic aperture radar, curvelet transform technology, applied in image data processing, instrument, character and pattern recognition, etc., can solve the target and background clutter distribution with poor reflection intensity, it is difficult to detect weak, small and hidden To achieve the effect of low false alarm probability, improved detection rate, and strong anti-false alarm ability
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[0051] Now the present invention will be further described in conjunction with the embodiments, taking the measured SAR image as an example, the present invention will be further elaborated:
[0052] Step 1: Input the original SAR image.
[0053] Step 2: Calculate the statistical properties of the entire SAR image. The statistical characteristic indicators used here are the mean value, accurate deviation and entropy value. In fact, the first-order statistical characteristics mainly reflect the amount of image information and the degree of deviation.
[0054] Step 3: Determine the gray value gap between the target area and the background clutter area in the image, the purpose is to determine whether the input SAR image has a high SNR or a low SNR image. First determine the target area, then extract the target area through mask or image segmentation technology, calculate the mean value of the sub-image of the target area, that is, the mean value of the local target area, and co...
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Abstract
Description
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