Dim and Small Target Detection Based on Gaussian Markov Random Field Motion Direction Estimation

Abstract

To improve the detection of dim and small targets in low signal-to-noise ratio (SNR<3dB) scenarios, energy accumulation along the estimated direction by estimating the direction of the target's motion at different moments is a proven approach, and considering that the Gaussian Markov random field can well describe the correlation of image element points in their air and time domains, Gaussian Markov random field is introduced into the paper to process the image. Firstly, by combining the proximity of image element spatial coordinates and the correlation of image element values to construct a new adaptive Gaussian weighted Markov random field filtering model, which fully incorporates the spatio-temporal information of image element coordinates and the correlation degree information of image element values for weighting to describe the image element neighborhood system. Second, to further enhance the energy of weak targets, the energy accumulation of targets is achieved by establishing a Gaussian Markov random field motion direction estimation model on the basis of obtaining filtered images. Through experiments, it is shown that the algorithm proposed in this paper can effectively accumulate the weak signal of the target between frames, and the target signal-to-noise ratio can reach more than 6dB compared with that before enhancement, effectively improving the detection ability of weak targets.INDEX TERMS Dim and small targets; detection; Gaussian Markov random field; energy accumulation; motion estimation

Dim and Small Target Detection Based on Spatio-Temporal Filtering and High-Order Energy Estimation

“…The higher the false alarm rate, the more serious the noise interference is, which is not conducive to the detection and extraction of targets. The specific calculation formula is as follows [37].…”