This paper presents a method for recognition of Noisy Subsurface Images using Morphological Associative Memories (MAM). MAM are type of associative memories that use a new kind of neural networks based in the algebra system known as semi-ring. The operations performed in this algebraic system are highly nonlinear providing additional strength when compared to other transformations. Morphological associative memories are a new kind of neural networks that provide a robust performance with noisy inputs. Two representations of morphological associative memories are used called M and W matrices. M associative memory provides a robust association with input patterns corrupted by dilative random noise, while the W associative matrix performs a robust recognition in patterns corrupted with erosive random noise. The robust performance of MAM is used in combination of the Fourier descriptors for the recognition of underground objects in Ground Penetrating Radar (GPR) images. Multiple 2-D GPR images of a site are made available by NASA-SSC center. The buried objects in these images appear in the form of hyperbolas which are the results of radar backscatter from the artifacts or objects. The Fourier descriptors of the prototype hyperbola-like and shapes from non-hyperbola shapes in the sub-surface images are used to make these shapes scale-, shift-, and rotation-invariant. Typical hyperbola-like and non-hyperbola shapes are used to calculate the morphological associative memories. The trained MAMs are used to process other noisy images to detect the presence of these underground objects. The outputs from the MAM using the noisy patterns may be equal to the training prototypes, providing a positive identification of the artifacts. The results are images with recognized hyperbolas which indicate the presence of buried artifacts. A model using MATLAB has been developed and results are presented.
Proc. SPIE. 4885, Image and Signal Processing for Remote Sensing VIII
KEYWORDS: Radar, Detection and tracking algorithms, Visualization, Image filtering, Neural networks, Image classification, Filtering (signal processing), 3D image processing, Evolutionary algorithms, General packet radio service
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 non-parabola 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 N8(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. 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.