Optimal scale selection is the key step of the slope segmentation. Taking three geomorphological units in different parts of the loess as test areas and 5 m-resolution DEMs as original test date, this paper employed the changed ROC-LV (Lucian, 2010) in judging the optimal scales in the slope segmentation process. The experiment results showed that this method is effective in determining the optimal scale in the slope segmentation. The results also showed that the slope segmentation of the different geomorphological units require different optimal scales because the landform complexity is varied. The three test areas require the same scale which could distinguish the small gully because all the test areas have many gullies of the same size, however, when come to distinguish the basins, since the complexity of the three areas is different, the test areas require different scales.
L. P. Zhang and Z. Z. Ma, “The Research on the Relation between Gully Density and Cutting Depth in Different Drainage Landform Evolution Periods,” Geographical Re- search, Vol. 17, No. 3, 1998, pp. 273-278.
X. N. Xiao, L. Z. Cui, C. Wang, et al., “Analysis of Spa- tial Data for Simulating the Development Process of Topographic Feature of Watershed,” Scientia Geographical Sinica, Vol. 24, No. 4, 2004, pp. 439-443.
G. A. Tang, F. Y. Li, X. J. Liu, et al., “Research on the Slope Spectrum of the Loess Plateau,” Science in China Series E: Technological Sciences, Vol. 51, Supp. 1, 2008, pp. 175-185. doi:10.1007/s11431-008-5002-9
Y. Zhou, G. A. Tang, X. Yang, et al., “Positive and Nega- tive Terrains on Northern Shaanxi Loess Plateau,” Journal of Geographical Sciences, Vol. 20, No. 1, 2010, pp. 64- 76. doi:10.1007/s11442-010-0064-6
P. T. Giles and S. E. Franklin, “An Automated App- Roach to the Classification of the Slope Units Using Di- gital Data,” Geomorphology, Vol. 21, No. 3, 1998, pp. 251-264. doi:10.1016/S0169-555X(97)00064-0
P. M. Atkinson and P. J. Curran, “Defining an Optimal Size of Support for Remote Sensing Investigations,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No. 3, 1995, pp. 768-776. doi:10.1109/36.387592
T. S. Roger, S. Georges and L. Jean, “Using Color, Texture, and Hierarchical Segmentation for High-Resolution Remote Sensing,” ISPRS Journal of Photogrammetric & Remote Sensing, Vol. 63, No. 2, 2008, pp. 156-168.
J. L. Silvan-Cardenas, L. Wang and F. B. Zhan, “Repre- senting Geographical Objects with Scale-Induced Indeter- Minate Boundaries: A Neural Network-Based Data Mo- del,” International Journal of Geographical Information Science, Vol. 23, No. 3, 2009, pp. 295-318.
C. E. Woodcock, A. H. Strahler and D. L. B. Jupp, “The Use of Variograms in Remote Sensing II: Real Digital Images,” Remote Sensing of Environment, Vol. 25, No. 5, 1988, pp. 349-379. doi:10.1016/0034-4257(88)90109-5
L. Drǎgu?, D. Tiede and S. R. Levick, “ESP: A Tool to Estimate Scale Parameter for Multiresolution Image Seg- mentation of Remotely Sensed Data,” International Jour- nal of Geographical Information Science, Vol. 24, No. 6, 2010, pp. 859-871. doi:10.1080/13658810903174803
M. Kim and T. Warner, “Estimation of Optimal Image Object Size for the Segmentation Stands with Multispectral IKONOS Imagery,” In: T. Blaschke, S. Lang and G. J. Hay, Eds., Object-Based Image Analysis-Spatial Concepts for Knowledge Driven Remote Sensing Application, Springer, Berlin, 2008, pp. 291-307.