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The essence of weak and small target detection is to achieve separation between the target and the background. However, for extremely low contrast (<0.1) and extremely low signal-to-noise ratio (<=3dB, most of which are less than 0dB) targets in strong backgrounds, the intensity difference between the target and the background is very small. However, in strong backgrounds, the fluctuation of the target submerges it and makes it difficult to directly separate it. Therefore, in order to achieve target detection in such extreme scenarios, it is necessary to construct feature descriptions that can effectively separate them. Based on this idea, this article summarizes the target detection task as a local feature difference maximization model suitable for all spatial target detection, and uses the local maximum Pearson correlation coefficient as the feature extraction equation to calculate the correlation between the two patches. Based on the small correlation between the target and the local background, and the high correlation between the background and the background, the separation of the target and the background is completed. Then using a constant local signal-to-noise ratio feature extraction equation to enhance the Pearson correlation results. A large number of experimental results show that the model and algorithm proposed in this paper can effectively detect targets with extremely low signal-to-noise ratios in strong backgrounds.
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