Development of Unsupervised Classification Techniques for Water Quality Analysis
Abstract
In large thematic studies, acquiring water quality data is costly and time-consumptive. For this reason, remote sensing is used as an initial estimation technique to identify significant environmental parameters. As shown in Cox et. al. (1998), correlation/regression may be used as a statistical tool to link the radiometric measures (acquired by the remote sensing platform) to the in situ measures (acquired by field sampling). This approach is both sensible and highly effective when an adequate sampling density is used that provides significant coverage for the study region. However, most studies cannot provide this coverage since the acquisition is far too costly and/or time-consumptive to meet the stochastic measures for sampling accuracy and precision. In addition, the related problem of how best to design a spatial sample within a highly diverse system (heterogeneous environmental strata) is an ongoing field of research (Bruce and Gao 1998). This technical note examines an innovative method of performing unsupervised classification that requires no a priori field sampling. The approach is self-contained and is not statistically biased by a priori field samples. However, the technique is highly conducive to the use of an a posteriori sample of field data to refine the unsupervised classes. In this regard, the technique may be used to: (1) estimate coarse surface features that require detailed field sampling, and (2) locate unusual patterns from within large regions that require in-depth field sampling to identify cause-and-effect relationships. The unsupervised method differs significantly from traditional methods applied within commercial image processing applications (Smith and Cole 1999). The approach does not use random sampling and is not based upon equal area sampling. In addition, no a priori assumptions are made concerning the normality (symmetry, kurtosis) of the underlying image data.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2000
- Accession Number
- ADA378497
Entities
People
- Perry Lapotin
- Robert H. Kennedy