Analysis of remote sensing imagery in the past has consisted of processing a single image from a single sensor. Capabilities now exist to use more than one data source in a single interpretation (such as multi-sensor integration, multi-temporal analysis, interpretations of combined raster-vector overlays). As more data sources become available, data sets become more pluralized, creating two key challenges in the related analysis. The first challenge is to integrate and manage the physical data and metadata as individual data sources and as compound datasets. The second challenge is to select data subsets and related analysis methodologies which are best suited to solve given problems. It is obvious that a dataset consisting of both an optical image and a microwave image will require different analysis methods for each subset, to meet similar goals. In the less extreme case of a dataset containing both spaceborne and airborne optical images, different methods would apply due to their different resolutions and spectral properties.
Current and potential operational users could have very high demands for remote sensing data if they were provided with appropriate interpretation tools. These users would not be interested in the process of choosing analysis methods for their data, they would be more concerned with which method was actually used, and its result compared to the specified input criteria. Ultimately they would not be concerned with even choosing the data, but rather would provide a goal of their desired result for driving the decision of which data to select.
In this paper, a technique is presented for addressing high-level criteria for analyzing remote sensing data. The technique consists of selecting methods most likely to achieve the result from among several known candidates.