The Oak Ridge Research Reactor (ORRR) was operated as an isotope production and irradiation facility from March
1958 until March 1987. The U.S. Department of Energy (DOE) permanently shut down and removed the fuel from the
ORRR in 1987. The water level must be maintained in the ORRR pool as shielding for radioactive components still
located in the pool. The DOE Office of Environmental Management (EM) needs to decontaminate and demolish the
ORRR as part of the Oak Ridge cleanup program. In February 2004, increased pit corrosion was noted in the pool's 6-
mm (¼")-thick aluminum liner in the section nearest where the radioactive components are stored. If pit corrosion has
significantly penetrated the aluminum liner, then DOE EM must accelerate its decommissioning and demolition (D and D)
efforts or look for alternatives for shielding the irradiated components. The goal of Mississippi State University's
Institute for Clean Energy Technology (ICET) is to provide a determination of the extent and depth of corrosion.
Results from the work will facilitate ORNL in making reliable disposition decisions.
ICET's inspection approach is to quantitatively estimate the amount of corrosion using Fourier transform profilometry
(FTP). FTP is a non-contact 3-D shape measurement technique. By projecting a fringe pattern onto a target surface and
observing its deformation due to surface irregularities from a different view angle, FTP is capable of determining the
height (depth) distribution of the target surface, thus reproducing the profile of the target accurately. ICET has
previously demonstrated that its FTP system can quantitatively estimate the volume and depth of removed and residual
material to high accuracy.
Soil texture is an important physical property of soil that affects many agricultural activities. It describes soil composition in terms of the relative proportion of three typical sized particles, i.e., clay, silt and sand. Traditional soil texture analysis methods involve inefficient physical and chemical processing procedures. To improve the efficiency for the analysis, previously we proposed a wavelet frame based image analysis system that related textural patterns observed at soil surface to the particle compositions. The system was capable of differentiating between 33 soil samples in terms of three categories with a 91% success rate. However, it required image acquisition under two camera settings. In this paper, we further our investigation with an improved image analysis approach, in which Gabor wavelets are utilized to generate textural features. Experiments showed that a combination of analysis results from two groups of Gabor wavelets yielded a 91% classification accuracy. Although the accuracy remained unchanged, the Gabor wavelet based system provided improved efficiency and flexibility over the previous system in that it needs only one set of images acquired under a fixed camera setting. Moreover, an improved consistency between individual classification votes was observed with the new system, indicating a greater potential for a finer categorization of soil textures.
Surface roughness is an important physical property of soil in agricultural applications. It is a key parameter affecting the
optical reflectance of bare soils, which can be computed from imagery acquired with airborne or space-based remotesensing
devices. Accurate ground-truth roughness data need to be collected before a correct computational interpretation
can be made. This paper presents the development of a real-time, geo-referenced, ground-based imaging system that
produces quantitative ground truth information of soil surface roughness. The system applies Fourier transform
profilometry (FTP) to an image of a soil area under study to obtain relative height data of the surface. Then it computes
parameters such as root mean square (RMS) and correlation length as measures of roughness. Measurement experiments
have been carried out successfully both under simulated conditions in the laboratory and in the field. The results show
that the system is capable of generating reliable ground-truth soil surface roughness information. In comparison with
other approaches, this developed system is fast, efficient and inexpensive.
Soil texture is defined as the relative proportion of clay, silt and sand found in a given soil sample. It is an important physical property of soil that affects such phenomena as plant growth and agricultural fertility. Traditional methods used to determine soil texture are either time consuming (hydrometer), or subjective and experience-demanding (field tactile evaluation). Considering that textural patterns observed at soil surfaces are uniquely associated with soil textures, we propose an innovative approach to soil texture analysis, in which wavelet frames-based features representing texture contents of soil images are extracted and categorized by applying a maximum likelihood criterion. The soil texture analysis system has been tested successfully with an accuracy of 91% in classifying soil samples into one of three general categories of soil textures. In comparison with the common methods, this wavelet-based image analysis approach is convenient, efficient, fast, and objective.