Object-based classification has proved to increase the classification capabilities of the very high resolution imagery in the last years. The classification is performed with the use of segments that represent a homogenous group of pixels. While most of the segments attributes which classification is based on is computed as a parametric summary numeric value, the emphasis of this preliminary study was to analyze whether the empirical distribution functions (ECDF) as the segments additional attribute are sufficiently representative and effective separability measure. By empirical cumulative distribution functions are meant step functions where all the pixels inside of a segment are being considered. When distribution functions are interpreted as a segments spectral signature they can be used as a graphic separability tool or as an input parameter in a classification method where segments are compared using Kolmogorov-Smirnov distance and the two-sided Kolmogorov-Smirnov test. To test the between class spectral confusion and within class variability three band color infrared orthophoto imagery with 1 m resolution for the urban areas in Ljubljana, Slovenia, was used. Typical sealed and non-vegetation land use classes such as different rooftops, roads, bare-soil, shadows and water were examined. The experimental analysis showed that Kolmogorov-Smirnov classifier has the potential to differentiate spectrally similar classes and has proven to slightly improve the number of correctly classified segments. All algorithms are implemented in the IDL programming language which will enable the design and use of graphic user interface along with the ENVIs and ENVI EXs interface and their capabilities.Pages: 2392-239
