title: Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.
abstract: Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.
authors.affiliation: Shandong University of Science and Technology, Geomatics College, Qianwan port road 579, Qingdao, Shandong 266590, China.|Shandong University of Science and Technology, The Key Laboratory of Geomatics and Digital Technology, Qianwan port road 579, Qingdao, Shandong 266590, China.
authors.forename: Hongchun
authors.initials: H
authors.lastname: Zhu
authors.affiliation: Shandong University of Science and Technology, Geomatics College, Qianwan port road 579, Qingdao, Shandong 266590, China.
authors.forename: Lijie
authors.initials: L
authors.lastname: Cai
authors.affiliation: Shandong University of Science and Technology, College of Information Science and engineering, Qianwan port road 579, Qingdao, Shandong 266590, China.
authors.forename: Haiying
authors.initials: H
authors.lastname: Liu
authors.affiliation: Shandong University of Science and Technology, Geomatics College, Qianwan port road 579, Qingdao, Shandong 266590, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Huang
mesh_terms: D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval; D010363:Pattern Recognition, Automated / Q000379:methods*; D058998:Remote Sensing Technology