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Remote Sens. 2018, 10(4), 590; https://doi.org/10.3390/rs10040590

An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery

1
Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying and Mapping, 28 Lianhuachi Road, Beijing 100830, China
2
Institute for Photogrammetry, University of Stuttgart, Geschwister-Scholl-Str. 24D, 70174 Stuttgart, Germany
3
Department of Geoinformatics—Z_GIS, University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria
4
Remote Sensing Technology Institute (IMF), Earth Observation Center (EOC), German Aerospace Center (DLR), 82234 Weßling, Germany
*
Author to whom correspondence should be addressed.
Received: 16 January 2018 / Revised: 26 March 2018 / Accepted: 7 April 2018 / Published: 11 April 2018
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Abstract

Remote sensing (RS) image segmentation is an essential step in geographic object-based image analysis (GEOBIA) to ultimately derive “meaningful objects”. While many segmentation methods exist, most of them are not efficient for large data sets. Thus, the goal of this research is to develop an efficient parallel multi-scale segmentation method for RS imagery by combining graph theory and the fractal net evolution approach (FNEA). Specifically, a minimum spanning tree (MST) algorithm in graph theory is proposed to be combined with a minimum heterogeneity rule (MHR) algorithm that is used in FNEA. The MST algorithm is used for the initial segmentation while the MHR algorithm is used for object merging. An efficient implementation of the segmentation strategy is presented using data partition and the “reverse searching-forward processing” chain based on message passing interface (MPI) parallel technology. Segmentation results of the proposed method using images from multiple sensors (airborne, SPECIM AISA EAGLE II, WorldView-2, RADARSAT-2) and different selected landscapes (residential/industrial, residential/agriculture) covering four test sites indicated its efficiency in accuracy and speed. We conclude that the proposed method is applicable and efficient for the segmentation of a variety of RS imagery (airborne optical, satellite optical, SAR, high-spectral), while the accuracy is comparable with that of the FNEA method. View Full-Text
Keywords: remote sensing image segmentation; geographic object-based image analysis; graph theory; fractal net evolution approach; minimum spanning tree; minimum heterogeneity rule; message passing interface remote sensing image segmentation; geographic object-based image analysis; graph theory; fractal net evolution approach; minimum spanning tree; minimum heterogeneity rule; message passing interface
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gu, H.; Han, Y.; Yang, Y.; Li, H.; Liu, Z.; Soergel, U.; Blaschke, T.; Cui, S. An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery. Remote Sens. 2018, 10, 590.

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