A Method of Loess Landform Automatic Recognition Based on Slope Spectrum
Shuangli Liu · Geo-information Science · 2015
As a research hot- spot of modern geomorphology, landform recognition and classification are important in various study areas such as ecological environment, hydrology and geological structure analysis.Traditional recognition methods, which are inadequate to solve the linear inseparable problem of pattern recognition, exhibit a low accuracy in landform recognition. As a dynamic information processing system, neural network is capable to deal with linear inseparable in landform recognition. Slope spectrum is an effective method to reflect the macro terrain features with quantitative micro-terrain-indicators. It has been receiving widespread attentions in geomorphology. This paper introduces an automatic recognition method based on slope spectrum and neural network. Using DEM data of eight sample areas with different loess landform types in Shaanxi Province, ten small watersheds and their slope spectrums are extracted for each of the eight sample areas. Then,we calculate the slope spectrum indices of these eighty small watersheds and use the indices to construct BP neural network for loess landform automatic recognition. Among the eighty small watersheds, 60% of them are randomly selected as training samples and 40% of them are selected as verification samples. Recognition results show that the accuracy rate is 70% on average for the eight sample areas, and it would be raised to 80% or 85%when the landform types of Loess Hilly- gully or Loess Hill- ridge are eliminated from the eight sample areas respectively. This study indicates that slope spectrum is capable of handling the linear inseparable problem in landform recognition.