Next Article in Journal
Hygroscopicity of Different Types of Aerosol Particles: Case Studies Using Multi-Instrument Data in Megacity Beijing, China
Next Article in Special Issue
Multi-Layered Circular Dielectric Structures’ Synthetic Aperture Radar Imaging Based on Green’s Function Using Non-Uniform Measurements
Previous Article in Journal
Assessment of Mitigation Strategies for Tropospheric Phase Contributions to InSAR Time-Series Datasets over Two Nicaraguan Volcanoes
Open AccessArticle

Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree

by Wenjie Lin and Yu Li *
Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin 123000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(5), 783; https://doi.org/10.3390/rs12050783
Received: 30 January 2020 / Revised: 23 February 2020 / Accepted: 24 February 2020 / Published: 1 March 2020
With finer spatial scale, high-resolution images provide complex, spatial, and massive information on the earth’s surface, which brings new challenges to remote sensing segmentation methods. In view of these challenges, finding a more effective segmentation model and parallel processing method is crucial to improve the segmentation accuracy and process efficiency of large-scale high-resolution images. To this end, this study proposed a minimum spanning tree (MST) model integrated into a regional-based parallel segmentation method. First, an image was decomposed into several blocks by regular tessellation. The corresponding homogeneous regions were obtained using the minimum heterogeneity rule (MHR) partitioning technique in a multicore parallel processing mode, and the initial segmentation results were obtained by the parallel block merging method. On this basis, a regionalized fuzzy c-means (FCM) method based on master-slave parallel mode was proposed to achieve fast and optimal segmentation. The proposed segmentation approach was tested on high-resolution images. The results from the qualitative assessment, quantitative evaluation, and parallel analysis verified the feasibility and validity of the proposed method. View Full-Text
Keywords: minimum spanning tree; high-resolution image segmentation; minimum heterogeneity rule; multicore parallel processing; regionalized fuzzy clustering method minimum spanning tree; high-resolution image segmentation; minimum heterogeneity rule; multicore parallel processing; regionalized fuzzy clustering method
Show Figures

Graphical abstract

MDPI and ACS Style

Lin, W.; Li, Y. Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree. Remote Sens. 2020, 12, 783.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop