Region extraction-based two-parameter constant false alarm detection method

Abstract

The present invention discloses a region extraction-based two-parameter constant false alarm detection method to mainly solve the problems of low detection speed and parameter setting caused object missed detection in an existing SAR image object detection detection technology. The method comprises the following implementation steps: extracting positive and negative sample sets on trained images with target marks, training a specification gradient feature based template w by using a linear classifier SVM, and selecting an effective size set for the extracted positive sample set on the basis of an initial image size; then extracting an area under an effective size on a tested image on the basis of the template w and the effective size set; and detecting the extracted area by using a two-parameter constant false alarm so as to obtain a candidate area, inhibiting NMS of the candidate area by using a non-maximum value, and removing a large number of superimposed areas to obtain a finally remained area as a final detection result. Compared with conventional two-parameter CFAR detection, the detection method disclosed by the present invention has the advantages of high detection speed and high detection probability, and is applicable to rapid detection of SAR image objects.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a constant false alarm detection method, which can be used to quickly and effectively detect targets in synthetic aperture radar SAR images. Background technique [0002] Radar imaging technology was developed in the 1950s, and has been developed by leaps and bounds in the next 60 years. At present, it has been widely used in military, agriculture, forestry, geology, ocean, disaster, mapping and many other fields. [0003] Synthetic aperture radar (SAR) is an active sensor that uses microwaves for perception. Compared with other sensors such as infrared and optical, SAR imaging is not limited by conditions such as light and weather. SAR image automatic target recognition has received more and more attention. [0004] The ATR method of SAR automatic target recognition usually adopts the three-level processing flow proposed by the Lincoln Laboratory of the United States....

AI Technical Summary

Problems solved by technology

Since the algorithm needs to repeat the same processing for each pixel in the SAR image, the detection time of this method is too long. At the same time, the method needs to set the target window, protection window, and background window according to the prior information of the SAR image target. For targets with large size differences, unreasonable parameter settings will lead to inaccurate estimation of background clutter parameters, resulting in missed detection of targets. Targets, appearing in the same detection area, cause difficulties for subsequent processing after detection

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