Abstract
A method for segmenting deformable shapes of soil and ground layers in successive GPR image frames is described in this paper. First, pre-processing operators are applied to enhance the quality of each image frame. Second, local histogram features are used to initialize membership probabilities of each pixel in the current image frame. Then, a segmentation algorithm based on relaxation labeling is applied to perform image segmentation. This algorithm uses information from previous and current image frames to perform layer identification by formulating the segmentation task as a probabilistic relaxation labeling process in which the current frame image is used for initializing pixel membership probabilities estimated from gray-level histograms. The previous frame image is used for estimating the compatibility values to be utilized for segmenting the current frame image using mutual information among neighboring pixels. By iteratively refining the membership probabilities of each pixel in the current image frame in parallel, an enhanced segmentation is produced according to the refined probabilities. A distinguishing characteristic of this process is the ability to incorporate both temporal contexts (down-track history information encoded as compatibilities) and spatial contexts (current-scan pixel neighborhood information encoded as probabilities), concurrently. The segmented image is post-processed by further filtering operations and checking for highly unlikely decisions to produce the final segmentation.