Share Email Print

Journal of Applied Remote Sensing

Thresholding-based remote sensing image segmentation using mean absolute deviation algorithm
Author(s): Libao Zhang; Aoxue Li
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Simple and effective segmentation algorithms are required for remote sensing images because of their mass data and complex texture features. An algorithm based on minimum class mean absolute deviation (MCMAD) is proposed. First, a two-dimensional (2-D) histogram is constructed by a median filter and gray process. Second, by using a diagonal projection, the 2-D histogram of remote sensing images is transformed into a one-dimensional (1-D) histogram to decrease the computational complexity. Finally, class mean absolute deviation of each threshold in the 1-D histogram is calculated and the threshold corresponding to the MCMAD is considered as the optimal segmentation threshold. To improve performance, we introduce spectral information into the MCMAD algorithm and the results of spectral bands are combined to get final segmentation results. Because most of the background used in our experiment is vegetation, we introduce a normalized difference vegetation index band into our algorithm and use the MCMAD algorithm on it. Experimental results show that our algorithms not only perform better for remote sensing images but also meet time requirements.

Paper Details

Date Published: 27 October 2014
PDF: 13 pages
J. Appl. Remote Sens. 8(1) 083542 doi: 10.1117/1.JRS.8.083542
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Libao Zhang, Beijing Normal Univ. (China)
Aoxue Li, Beijing Normal Univ. (China)

© SPIE. Terms of Use
Back to Top