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Remote Sens. 2015, 7(5), 5980-6004; doi:10.3390/rs70505980

Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing100101, China
2
Geospatial Information Sciences, University of Texas at Dallas, Dallas, TX 75080, USA
3
China Mapping Technology Service Corporation, Beijing 100088, China
*
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 30 January 2015 / Revised: 27 April 2015 / Accepted: 29 April 2015 / Published: 13 May 2015
View Full-Text   |   Download PDF [3460 KB, uploaded 20 May 2015]   |  

Abstract

Segmentation, which is usually the first step in object-based image analysis (OBIA), greatly influences the quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem is that under-segmentation and over-segmentation always coexist at any scale. To address this issue, we propose a new method that integrates the newly developed constrained spectral variance difference (CSVD) and the edge penalty (EP). First, initial segments are produced by a fast scan. Second, the generated segments are merged via a global mutual best-fitting strategy using the CSVD and EP as merging criteria. Finally, very small objects are merged with their nearest neighbors to eliminate the remaining noise. A series of experiments based on three sets of remote sensing images, each with different spatial resolutions, were conducted to evaluate the effectiveness of the proposed method. Both visual and quantitative assessments were performed, and the results show that large objects were better preserved as integral entities while small objects were also still effectively delineated. The results were also found to be superior to those from eCongnition’s multi-scale segmentation. View Full-Text
Keywords: remote sensing image segmentation; region merging; multi-scale; constrained spectral variance difference; edge penalty remote sensing image segmentation; region merging; multi-scale; constrained spectral variance difference; edge penalty
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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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MDPI and ACS Style

Chen, B.; Qiu, F.; Wu, B.; Du, H. Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty. Remote Sens. 2015, 7, 5980-6004.

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