Examining lacunarity approaches in comparison with fractal and spatial autocorrelation techniques for urban mapping

The conventional spectral-based classification techniques have often been criticized due to the lack of consideration of images' spatial properties. This study evaluates and compares two lacunarity methods, fractal triangular prism, spatial autocorrelation, and original spectral band approaches in classifying urban images. Results from this study show that the traditional spectral-based classification approach is inappropriate in classifying urban categories from high-resolution data. The fractal triangular prism approach was also found to be ineffective in classifying urban features. Spatial autocorrelation was more accurate than the fractal approach. The overall accuracies in this study for the fractal, conventional spectral, spatial autocorrelation, lacunarity binary, and lacunarity gray-scale approaches were 52 percent, 55 percent, 78 percent, 81 percent, and 92 percent, respectively. These findings suggest that the lacunarity approaches are far more effective than the other approaches tested and can be used to drastically improve urban classification accuracy.