Hamed Parsiani

Application of Fourier descriptors and neural network to classification 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 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.

Recent advances in weighted finite automata fractals

A recent method in fractal compression is weighted finite automata (WFA) which are used for the approximation and subsequent compression of two-dimensional discrete functions such as images. Traditional fractal based methods look for similarities at two fixed levels of resolutions (or scales). Every block of the image (range) is approximated by a scaled version of other regions, called domains. The WFA tree structure permits the usage of domains at any scale, allowing a larger domain pool. Ranges are approximated by a linear combination of domains. In this work, an additional image-independent domain pool is used to further enhance compression, and similarities at different scales are exploited using a quadrature mirror filter (QMF) decomposition instead of the image itself. Our algorithm achieved (as an example) a very good quality (34.6 dB PSNR) at 14.4:1 compression.

UPRM Boundary Layer Data Improvements Implementing Variable Overlap Correction

At the UPRM Atmospheric Research Laboratory, the backscatter-power data received from the Lidar equipment is processed and used to construct a power profile. The Lidar has a trans mit/receive capability for the basic wavelengths of 355, 532, and 1064 nm. Recently, it has been expanded to provide Raman scattering power data at 387nm, and 407nm, the later being improved for signal to noise ratio enhancement. The power profiles are then used to determine various atmospheric parameters, including the fundamental aerosol backscatter coefficient. The overlap factor in a co-axial Lidar system is responsible for the inability of the telescope to focus the backscatter data originated at the lower alt itudes, hence, it will influence any product derived from this data. The method applied (Ansmann 2002) to determine the corrected-overlap profile of the UPRM Lidar equipment is based on the received Raman inelastic (387nm) and elastic (355nm) backscatter signals. It is an iterative approach that makes use of the fact that the deviation between the Kletts solution for the backscatter coefficient, which is calculated from the elastic backscatter signal, and the Raman Lidar solution, contains the information about the overlap factor. As a result, the determination of the overlap factor will improve backscatter power data at the lower alt itudes and provide a more accurate boundary layer detection. In this paper, the backscatter power correction has been applied to the Lidar three received wavelengths.

Application of Fourier descriptors and fuzzy logic to classification of radar subsurface images

This paper presents an application of Fourier Descriptors and Fuzzy Logic for the recognition of archeological artifacts in Ground Penetrating Radar (GPR) images of a surveyed site. 2-D GPR survey images of a site are made available by NASA-SSC center. The buried artifacts in these images appear in the form of hyperbolas which are the results of radar backscatter from the artifacts. The Fourier Descriptors of an image are applied as inputs to a Fuzzy C-Mean Classifier (FCMC). The FCMC algorithm has to recognize different types of shapes, in order to separate hyperbola-like shapes from non-hyperbola 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 obtaining an unsupervised classification by applying Fuzzy C-Mean clustering algorithm to the FD sequences. The classes obtained depend upon the requirements of the user, namely, two classes of hyperbola/no-hyperbola objects, or several classes from symmetric hyperbolas to total rejects could be obtained. The results consisting of recognized hyperbolas indicate the presence of buried artifacts. Also, our previous results of supervised FD-Neural Network (FD-NNC) published in the proceedings of SPIE 2002 are compared with unsupervised FD-FCMC. The compared results in terms of the quality of classification are presented in this work.

Application of morphological associative memories and Fourier descriptors for classification of noisy subsurface signatures

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.

High-resolution vegetation index as measured by radar and its validation with spectrometer

Changes in vegetation can affect our health, the environment and the economy. Understanding this, twenty years ago scientists began to use satellite remote sensors to monitor major fluctuations in vegetation and understand how it affects the environment. The pixel accuracy of some Synthetic Aperture Radar (SAR) satellites is now at near one meter resolution. A new formulation of vegetation index using such active sensors will greatly improve the Vegetation health accuracy. Attempt has been made by M. Tokunaga to relate ERS-SAR satellite sensor data of vegetation canopies to the LANDSAT TM satellite sensor measurements, both at 30 meter resolution. A correlation was observed above Normalized Difference Vegetation Index (NDVI) of 0.4, but their experiment was not based on the data taken by the two satellite sensors at the same time period. In this research, a correlation is determined between the active and passive measurements of the vegetation index, at very high resolution. The measurements take place at the near ground level over varied vegetation health, using a Ground Penetrating Radar (GPR), and a handheld Spectrometer. The GPR and the handheld Spectrometer have the same field of view, so it is possible to compare data for the whole range of NDVI. Both measurements take place one right after the other, to allow an accurate comparison. The goal of this research is to define a new vegetation index, using active sensors. The GPR operating at 1.5 GHz produces images that contain backscatter signals obtained from vegetation. These images are processed by a filter to eliminate clutter and noise. The Fourier amplitude and phase characteristics of the vegetation health are extracted from the backscatter signal. The same vegetation is subjected to the spectrometer measurements. Our results show a linear correlation between power of GPR backscatter signal and the NDVI as calculated by the spectrometer data. As a continuity of this work, the ground validation will be compared to the active/passive satellite sensors for the measurement of vegetation health.

Subsurface material type determination from ground-penetrating radar signatures

A Ground Penetrating Radar (GPR at 1.5 GHz) has been used to help determine the material type at different subsurface layers. Based on the incidence and reflected electromagnetic waves, a new method was devised which determines Material Characteristics in Fourier Domain (MCFD), which can be used for material identification. MCFD is calculated at every reflection. Each reflection is caused by sufficient change in dielectric constant of two different soil layers. Working in frequency domain the effect of the media is separated by using the wavelet of the electromagnetic signal before and after it is reflected from the media. An algorithm is developed which obtains the MCFD which defines material type by the use of a 2-layer Back-propagation Neural Network (NN). Material type can be determined irregardless of layer at which the object is buried (limited to GPR reflection intensity level), or object size, or if an extended subsurface layer is present. In this method, GPR images for different material types at different layers were obtained; up to two levels of mixed material types such as sand, clay, loam, rock, broken jar, etc. were considered.