[1]LI Bin-bing,HUANG Lei,LIU Da-wei.Research for Optimal Scale on the Remote Sensing Image Recognition of Gully Distribution in Loess Hilly-Gully Region[J].Research of Soil and Water Conservation,2014,21(04):158-162.

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Research for Optimal Scale on the Remote Sensing Image Recognition of Gully Distribution in Loess Hilly-Gully Region

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 21 Number of periods: 2014 04 Page number: 158-162 Column: Public date: 2014-08-28

Title:: Research for Optimal Scale on the Remote Sensing Image Recognition of Gully Distribution in Loess Hilly-Gully Region

Author(s):: LI Bin-bing, HUANG Lei, LIU Da-wei; Department of Information Engineering, University of Armed Police Forces of Engineering, Xi'an 710086, China

Keywords:: gully; high resolution spatial imagery; parameter selection; segmentation; recognition

CLC:: S157.1

DOI:: -

Abstract:: This paper brought forward a new method of optimal segmentation scale and threshold methods. maximum of objective function was established by using Moran's I index and intrasegment variance of GLCM and was tested in study area, the results of the optimal partition scale were 24, 31, 36, 42 for agriculture road, gully, cultivated land (slope, terrace) and the forestland in research area. Based on optimal segmentation scale selection, the threshold methods estimated by K-means cluster analysis and TwoStep Cluster using NDVI, BRIGHTNESS and GLCM were established to identify gully candidates and separate gullies from false positives. In order to find out whether the clusters are sufficiently different to represent the false positives, a t-test performed for GLCM, BRIGHTNESS showed that those tome parameters were significant at 95% of the confidence level, therefoe, suggested that the threshold of clusters were reliable and robust.

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