Knowledge acquisition is concerned with finding and structuring knowledge in such a way that it can be used in a variety of intelligent decision-making tools. Knowledge of a domain can be encoded as taxonomy i.e., a hierarchically organized set of categories. The relationships within the hierarchy can be of different kinds, depending on the application, and a typical taxonomy includes several different kinds of relations. Thus taxonomies play an important role in analyzing and modeling knowledge. The focus of this study is to derive knowledge from a standard taxonomic structure in the remote sensing domain. The various methodological channels adopted by the remote sensing data analysts to produce different information products normally go through some definite processes, which can be examined along with their context (spectral, spatial, temporal) by the taxonomical approach. This allows users to assess the applicability of a methodology for a particular area of interest and also has the advantage in aiding the upper-level decision-makers in understanding why different approaches might provide different outputs to the same source data. Some of the previous work done by a number of multi disciplinary researchers in analyzing remote sensing data has been used in this study to examine the structure of their methodologies from a taxonomical perspective. The analysis of the developed taxonomies clearly indicates a definite structure to the underlying analysis procedures and has potential for the development of systems to automate them.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-3744 |
| Date | 11 December 2004 |
| Creators | Kari, Swapna |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
Page generated in 0.0025 seconds