Edge Detection in UAV Remote Sensing Images Using the Method Integrating Zernike Moments with Clustering AlgorithmsReport as inadecuate




Edge Detection in UAV Remote Sensing Images Using the Method Integrating Zernike Moments with Clustering Algorithms - Download this document for free, or read online. Document in PDF available to download.

International Journal of Aerospace Engineering - Volume 2017 2017, Article ID 1793212, 7 pages - https:-doi.org-10.1155-2017-1793212

Research Article

Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China

Kunming Surveying and Mapping Institute, Kunming 650051, China

Correspondence should be addressed to Liang Huang

Received 18 September 2016; Revised 5 January 2017; Accepted 18 January 2017; Published 15 February 2017

Academic Editor: Paul Williams

Copyright © 2017 Liang Huang et al. 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.

Abstract

Due to the unmanned aerial vehicle remote sensing images UAVRSI within rich texture details of ground objects and obvious phenomenon, the same objects with different spectra, it is difficult to effectively acquire the edge information using traditional edge detection operator. To solve this problem, an edge detection method of UAVRSI by combining Zernike moments with clustering algorithms is proposed in this study. To begin with, two typical clustering algorithms, namely, fuzzy -means FCM and -means algorithms, are used to cluster the original remote sensing images so as to form homogeneous regions in ground objects. Then, Zernike moments are applied to carry out edge detection on the remote sensing images clustered. Finally, visual comparison and sensitivity methods are adopted to evaluate the accuracy of the edge information detected. Afterwards, two groups of experimental data are selected to verify the proposed method. Results show that the proposed method effectively improves the accuracy of edge information extracted from remote sensing images.





Author: Liang Huang, Xueqin Yu, and Xiaoqing Zuo

Source: https://www.hindawi.com/



DOWNLOAD PDF




Related documents