An Improved Algorithm for SAR Image Sea-Land Segmentation Based on 3D Maximum Between-Class Variance
Synthetic aperture radar(SAR)is a type of high-resolution imaging radar that operates in poor meteoro-logical conditions.Whether in the civilian or military field,the use of SAR for image target detection of ships at sea can play a key role.As a means of SAR image preprocessing,sea-land segmentation can lay a good foundation for the subse-quent research on target detection,recognition and tracking technology.In this paper,an improved sea-land segmenta-tion algorithm for SAR images with complicated island and shore backgrounds is proposed on the basis of the traditional three-dimensional maximum between-class variance algorithm,which changes the median value of the third-dimen-sional neighborhood of the original algorithm to the gradient operation of the Prewitt operator,which is suitable for pro-cessing images with complicated grayscale gradients,and can better segment SAR images.Considering that adding one dimension to the dimension can reduce the efficiency of the algorithm,the idea of decomposition is used to split the three-dimensional maximum between-class variance into three one-dimensional maximum between-class variance algo-rithms.Through experiments with two sets of SAR images,the results show that the improved three-dimensional max-imum between-class variance algorithm proposed in this paper is feasible,and it is better than the one-dimensional and two-dimensional maximum between-class variance algorithms in terms of sea-land segmentation effect,and better than the traditional three-dimensional maximum between-class variance algorithm in terms of computational complexity.
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