Radar target and shadow segmentation method based on wavelet and constant false alarm rate

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...

AI Technical Summary

Problems solved by technology

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 to be adjusted, making it difficult to achieve optimization.

The SAR image segmentation method based on edge detection is greatly affected by the speckle noise in the SAR image. For example, in the case of a lot of speckle noise, it is often difficult for the edge detection operator to obtain a better edge map, which makes it difficult to accurately position the edge pixels. position

Moreover, in the existing SAR image segmentation methods, the image segmentation is generally carried out for the classification of objects of interest or objects, and rarely involves the segmentation of target shadows.

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