Information extraction of typical karst landform based on RS

Shufen Huang; Anjun Lan; Jiaqiong Ma; Haixiang Guo

doi:10.1117/12.2063696

14 May 2014 Information extraction of typical karst landform based on RS

Shufen Huang, Anjun Lan, Jiaqiong Ma, Haixiang Guo

Proceedings Volume 9158, Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China; 91580V (2014) https://doi.org/10.1117/12.2063696
Event: Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, 2012, Wuhan, China

Abstract

Guizhou Province is the most typical karst landform area of Southwest China Karst, and how to exactly extract the typical karst landform information is important to the economic development of Guizhou. Not any method based on Remote Sensing (Hereinafter referred to as RS) to extract the karst landform were reported or published. For obtaining the accuracy information of karst landform, 10 meters resolution ALOS image is used to extract the karst landform information in Guanling County of Guizhou Province in this paper. The multiscale segmentations of RS images were finished and typical of karst landform in case study area were classified with the different segmentation rules created on the eCognition Developer platform. For mostly improving the accuracy of extraction information, the experiment areas are focused on the fengcong depressions, fengcong valleys, and fenglin basins. The results show that the fengcong depressions, fengcong valleys, and fenglin basins can be respectively well extracted from the images when the segmentation scale are respectively 280, 480 and 200, shape parameter is 0.8, and tightness parameter is 0.5. We believed the research would provide an important reference to extract the karst landform information in whole Guizhou, China or global level.

Citation Download Citation

Shufen Huang, Anjun Lan, Jiaqiong Ma, and Haixiang Guo "Information extraction of typical karst landform based on RS", Proc. SPIE 9158, Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, 91580V (14 May 2014); https://doi.org/10.1117/12.2063696

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