Improved signal processing and sensors for classifying clutter and unexploded ordnance (UXO) in areas with overlapping signatures are common is needed. This project investigated the effectiveness of signal processing techniques based on wavelets (1) to improve the signal to noise ratio and extract additional information from the signals and (2) as part of a probability-based approach for classification.
Applying probabilistic methods for classification involves several steps. First, a set of descriptors that adequately distinguishes between classes must be identified. Next, the statistical properties of the descriptor set for each class must be determined, usually by measuring the descriptors for samples taken from members of each class and calculating the usual statistical quantities such as the average and standard deviation. Finally, a statistical test must be developed using the descriptor statistical properties to determine class membership of an unknown sample.
This project’s original intent was to perform all three steps for a limited number of samples of UXO and clutter. Unfortunately, because of unexpected difficulty in performing the wavelet filtering portion of the project, the researchers focused only on the identification of the descriptor set. It has been the researchers’ experience that if an adequate descriptor set can be identified (i.e., one that can distinguish between the classes) then the other two steps involved in applying probabilistic classification methods will present little difficulty. By focusing efforts on the key step in the process, the researchers can effectively evaluate the potential for applying probabilistic methods for classification.
The results of this project are as follows:
- Wavelet filtering removes more noise at frequencies above 6 Hz than standard filtering. For the analyzed noise sample, it was found that wavelet filtering removed approximately 34% more noise in the 6 to 7 Hz frequency range than did standard filtering.
- Wavelet filtering preserves more useful information in the 4 to 6 Hz frequency range than standard filtering. For the analyzed noise signal, it was found that wavelet filtering preserved approximately 42% more signal in the 4 to 6 Hz frequency range than did standard filtering.
- Wavelet filtering is simpler and requires less user intervention than current filtering. It is expected that wavelet filtering can be further simplified by incorporating the filtering into a Geosoft module. This reduction in complexity would result in significant savings in both time and cost, especially if large areas are surveyed.
- The preliminary work done during this study encouraged the project team that a descriptor set for classifying UXO and clutter can be identified. This descriptor set will most likely include the descriptors used in this study, as well as additional descriptors. If such a descriptor set can be identified, it should be possible to use these descriptors with probabilistic methods to classify a magnetic anomaly as being either UXO or clutter.
The researchers recognize that the number of UXO and clutter samples prevents a statistically valid demonstration on the ability of the descriptor set for classification. However, because the limited results are so unambiguous, there is potential for identifying a descriptor set that will accurately classify UXO and clutter.
Based on this project’s results, the researchers believe that both wavelet filtering and application of probabilistic methods for classification have potential for improving the detection and identification of UXO. Potential follow-on work includes:
- Fully developing the wavelet filtering technique and incorporating the final wavelet filtering into a Geosoft module. This module would be made available to the UXO community and should result in the extraction of more useful information from the measured data than is currently being obtained. In addition to the improvement in filtering performance, it is believed that the use of wavelet filtering will reduce the time, manpower, and cost of filtering the large data sets typically collected by airborne UXO surveys.
- Fully exploring the potential of wavelet-based descriptors and probabilistic methods for classification. The preliminary work appears promising, but is only a start in evaluating the potential of this approach. Evaluation using larger data sets involving more examples of UXO and clutter is needed to determine if an effective descriptor set can be identified. If successful, this approach would form the basis for a screening of magnetic anomalies. A reduction in the time, manpower, and cost of evaluating the survey results would be achieved.