Application of Fourier Descriptors and Neural Network to classification of underground images

This paper presents an application of Fourier Descriptors and Neural Network for the recognition of archeological artifacts in Ground Penetrating Radar (GPR) images of a surveyed site. Multiple 2-D GPR images of a site are made available by NASA-SSC center. The buried artifacts in these images appear in the form of parabolas which are the results of radar backscatter from the artifacts. The Fourier Descriptors of an image are applied as inputs to a feed-forward backpropagation Neural Network Classifier (NNC). The NNC algorithm was trained to recognize parabola-like shapes from nonparabola shapes in the sub-surface images. The procedure consisted of removing background noise using a suitable threshold filter, locating the separate shapes in the image using N-8(p) connectivity algorithm, calculating a short sequence of Fourier Descriptors (FD) of each isolated shape, and finally classifying parabola/no-parabola using Neural Network applied to the FDs(1). The results are images with recognized parabolas which indicate the presence of buried artifacts. As a useful feature to archeologists, a 3-D Visualization of the complete survey area is produced using C++ and Visualization Tool Kit. The Algorithms for removing the background noise, thresholding, calculating the Fourier Descriptors, and obtaining a classification using a Neural Network were developed using Matlab.