Conclusions and results achieved A set of rigorous definitions for composite features in outdoor environments was developed. It was found that there is a unique pixel value pattern when a ridge line appears in a digital picture and this pattern can be extracted using a pattern recognition algorithm. After examining the U.S. Geological Survey data available, it was clear that the data is smoothed to the point that many land features commonly recognized by a human observer, such as cliffs and outcroppings of rock are lost in the smoothing. Although the U.S. Geological Survey data might be of more value if it included more data using smaller intervals than 30 meters, the additional data would then add to any required processing time. It should therefore be noted that using the USGS data for localization on a map will require that large scale features be utilized rather than some of the smaller distinguishable features so often used by humans.
Weather conditions can change the view significantly.
For example, when the sky is overcast, the image is dark, leading to a smaller range of pixel values and less likelihood of picking up differences that signify ridge lines. A scaling of the pixel values can often widen the spread, but only if there are not too many outliers to skew the distribution. As an example, an image taken with an overcast sky might contain pixels in the range of 0 to 150. By scaling the pixel values, the range can be changed to 0 to 255, providing a larger difference in values along the ridge lines. However, a patch of white in the image, such as a piece of pavement in the foreground, produces outliers and will skew the distribution and mitigate the effect of the scaling.