Radar target and shadow segmentation method based on wavelet and constant false alarm rate
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A technology of constant false alarm rate and radar target, applied in image analysis, image data processing, instruments, etc., can solve the problems of large amount of data, not considering spatial information, difficult to optimize, etc., to achieve strong universality and large application Foreground effect
Inactive Publication Date: 2015-12-16
PLA SECOND ARTILLERY ENGINEERING UNIVERSITY
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The advantage of the CFAR detection and segmentation method is that the segmentation speed is fast, but the disadvantage is that only the gray information of the image is considered, and the spatial information is not considered. Therefore, the segmentation results often contain speckle noise, which cannot meet the actual needs.
Although the SAR image segmentation method based on the MRF model considers the spatial neighborhood structure of each pixel, the defects are also obvious: the amount of data to be processed is large, the convergence speed of the method is slow, and multiple parameters need
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[0068] 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:
[0069] Step 1: Input the original SAR image.
[0070] Step 2: Select the Db4 wavelet function in the Daubechies wavelet family. Because it has better denoising performance, its decomposition and reconstruction of filter coefficients are relatively simple. At the same time, the stationary wavelet decomposition is selected, because compared with the ordinary two-dimensional discrete wavelet, the stationary wavelet avoids downsampling, and the size of the sub-image is the same as that of the original image, which is beneficial to the follow-up work.
[0071] Step 3: The key step in the wavelet multi-scale decomposition of the image is the determination of the decomposition scale N. If the decomposition scale is too low, the advantages of wavelet decomposition cannot be fully utilized. If ...
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Abstract
The invention relates to a radar target and shadow segmentation method based on wavelets and constant false alarm rates. The method includes the following eleven steps: inputting a synthetic aperture radar image; selecting a wavelet function; conducting wavelet multi-dimensioned decomposition; selecting a high frequency sub-image after wavelet decomposition; inputting a CFAR detector I; segmenting the selected sub-image; performing wavelet inverse transformation; removing a mean value; inputting a CFAR detector II; inputting a target that finally needs to be segmented; and segmenting the image again. The advantages of the method are that the influence of spot noise on an SAR image is reduced; the defect that a contrast ratio between a target and a background has to be high in terms of a single CFAR detection method is overcome, and a target zone and a shadow zone can be detected at the same time; weak scattering targets in the SAR image can be effectively segmented, and the method exhibits high universality. An experiment shows that the mentioned SAR image segmentation method based on CFARs and wavelet transformation is a feasible and effective segmentation method, and has great application prospects.
Description
technical field [0001] The invention belongs to the technical field of signal and information processing, and relates to a wavelet and constant false alarm rate radar target and shadow segmentation method combining wavelet transform, target detection, synthetic aperture radar imaging and synthetic aperture radar image characteristics. Background technique [0002] Image segmentation is not only an important content of image processing, but also the basic knowledge and key technology of image analysis, image understanding and image pattern recognition. Therefore, image segmentation has been widely used in many fields, such as biomedicine, remote sensing mapping, video communication, aerospace, public safety, archives management, transportation, agriculture, environment, ecology, geology, ocean, meteorology, disasters, etc. , rescue, etc., and with the continuous development and improvement of image segmentation theory and method technology, its application in practice is also...
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