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High resolution geographic imagery and its impact on GIS
Information (and its extraction) is the key element
As mentioned above, high-resolution imagery from both aerial and space borne sensors provides a challenge to the user community in terms of information extraction. The human eye and brain can identify objects in the image but the computer finds it difficult. If we cannot automate this process, then we will most certainly lose out on some of the major economic benefits of the imagery. If the human brain can do it, why can’t the computer? Well it actually can if it uses rules or knowledge based processing, just as the human brain does. The brain can make a decision on an image very quickly by understand and using context. If we see grassland in the center of an urban development, we can easily decide that it is a park, as opposed to agricultural land. To make this decision we are using knowledge and experience to create expertise and computer based expert systems are beginning to emerge that mimic this process. For many years, expert systems have been used successfully for medical diagnoses and various information technology (IT) applications but only recently have they been applied successfully to GIS applications. Statistical image processing routines, such as maximum likelihood and ISODATA classifiers, work extremely well at performing pixel-by-pixel analyses of images to identify land-cover types by common spectral signature. Expert-system technology takes the classification concept a giant step further by analyzing and identifying features based on spatial relationships with other features and their context within an image. Expert systems contain sets of decision rules that examine spatial relationships and image context. These rules are structured like tree branches with questions, conditions and hypotheses that must be answered or satisfied. Each answer directs the analysis down a different branch to another set of questions. 
The beauty of an expert system is that— because the rules, also called a knowledge base, are created by true experts, such as foresters or geologists— the system can be used successfully by non-experts. In terms of satellite images, the knowledge base identifies features by applying questions and hypotheses that examine pixel values, relationships with other features and spatial conditions, such as altitude, slope, aspect and shape. Most importantly, the know ledge base can accept inputs of multiple data types, such as digital elevation models, digital maps, GIS layers and other pre-processed thematic satellite images, to make the necessary assessments. In forestry, for example, an expert classification might identify one stand of trees as a specific species because they grow only at certain elevations and on southwest -facing slopes of less than 30 degrees. Another region within the image having similar spectral values might be interpreted as grass because it only occurs next to roadways in suburban areas. And another category may be labeled as an orchard because the trees grow in regular patterns. Because many of these examples rely on information contained in data other than satellite images, it’s easy to understand that expert system-technology is more of a decision-support tool than merely an image classifier. In fact, a satellite image isn’t even necessary. With the help of expert system-technology, the military already has benefited from cross-country mobility knowledge bases that consider soil type, land cover, elevation data and current weather reports to determine optimal routes for a certain type of vehicle to traverse an area. The beauty of the expert system however is that whenever new sources of information become available, they can be easily incorporated. For example, even though the mobility analysis can be carried out without imagery, the accuracy of the analysis can be affected by the ground conditions. If a satellite image can be used to extract moisture content (i.e. the “mud” factor), then it can be added to the knowledge base and used as part of a rule. One other key element of the experts system is the “traceability” of the process. Figure 3 shows that by simply querying the resultant map, the rule that was used to create the output can be displayed and verified. ERDAS IMAGINE was the first GIS oriented imaging system to be released with a Knowledge Based Classifier and it is being widely used throughout the world to automate many GIS decision-making processes. 
Imagery or Information The successful usage of imagery in a GIS is dependent upon a number of factors:
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