Discriminating Tailing Piles using VNIR Imagery and SVMs
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
This report describes a brief investigation into the use of multispectral satellite imagery and a machine learning technique to discriminate between the soils in two tailing piles in the U1a Complex at the Nevada National Security Site. The south pile dates from the late 1960s and the north pile dates from the early 2000s and is where recent tailings have been dumped. Our approach is to use commercially available satellite imagery of the U1a tailing piles and a Support Vector Machine (SVM) to discriminate between the soils in these piles. Experiments were performed using multispectral pixels selected from around the U1a Complex to train the support vector machine. Pixels in the scene were then classified by the SVM. Classification performance was assessed qualitatively through study of the classification imagery output by the SVM. These qualitative assessments provide the conclusions drawn by the investigation: 1. A SVM can be used to discriminate between different types of materials in satellite imagery. 2. The VNIR imagery used in this investigation does not provide the features needed by the SVM to discriminate between the two tailing piles at the U1a Complex-the soils look too similar in these spectral bands to discriminate. The remainder of the report details the experiments performed to reach these conclusions and the limitations and caveats associated with the conclusions.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC52-07NA27344
- OSTI ID:
- 1573148
- Report Number(s):
- LLNL--TR-791925; 991396
- Country of Publication:
- United States
- Language:
- English
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