A Study of the Segmentation Scale of High-resolution Remotely Sensed Data in Chengdu Plain
LIN Xian-cheng 1,2, LI Yong-shu 1
1. GIS Engineering Center of Southwest Jiaotong University, Chengdu 610031, China;2. College of Geography and Resources Sciences,Sichuan Normal University, Chengdu 610068, China
In classifying the high-resolution remotely sensed data,the method applying the object-oriented image analysis is better than that applying the traditional pixel-oriented analysis. The first key step is the image segmentation for applying the object-oriented analysis. The results of the segmentation are the acquisition of a series of objects related to the real objects. The veracity of the segmentation is related to the selected segmentation scale. This paper has studied the selection of the segmentation scale of the high-resolution remotely sensed data obtained in Chengdu plain, applied different scales for image segmentation and compared the results. It is found that the best scale is 30, the same as the scale with which the mean brightness of the image objects has the maximum standard deviation.
林先成, 李永树. 成都平原高分辨率遥感影像分割尺度研究[J]. 国土资源遥感, 2010, 22(2): 7-11.
LIN Xian-Cheng, LI Yong-Shu. A Study of the Segmentation Scale of High-resolution Remotely Sensed Data in Chengdu Plain. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(2): 7-11.