Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

• Published in: PloS one Vol. 11; no. 6; p. e0158585
• Main Authors: Zhu, Hongchun, Cai, Lijie, Liu, Haiying, Huang, Wei
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.06.2016; Public Library of Science (PLoS)
• Subjects: Accuracy; Classification; Computer and Information Sciences; Detection; Earth Sciences; Engineering and Technology; Heterogeneity; High resolution; Homogeneity; Image processing; Image Processing, Computer-Assisted - methods; Image resolution; Image segmentation; Information processing; Information retrieval; Information science; Information Storage and Retrieval; Object oriented; Pattern Recognition, Automated - methods; Physical Sciences; Process parameters; Quantitative analysis; Remote sensing; Remote Sensing Technology; Research and Analysis Methods; Science; Segmentation; Shape factor; Beijing China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0158585

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.