CFP last date
22 April 2024
Reseach Article

Remote Sensing Image Segmentation using OTSU Algorithm

by CH. V. V. S. Srinivas, M. V. R. V. Prasad, M. Sirisha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 12
Year of Publication: 2019
Authors: CH. V. V. S. Srinivas, M. V. R. V. Prasad, M. Sirisha
10.5120/ijca2019918885

CH. V. V. S. Srinivas, M. V. R. V. Prasad, M. Sirisha . Remote Sensing Image Segmentation using OTSU Algorithm. International Journal of Computer Applications. 178, 12 ( May 2019), 46-50. DOI=10.5120/ijca2019918885

@article{ 10.5120/ijca2019918885,
author = { CH. V. V. S. Srinivas, M. V. R. V. Prasad, M. Sirisha },
title = { Remote Sensing Image Segmentation using OTSU Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2019 },
volume = { 178 },
number = { 12 },
month = { May },
year = { 2019 },
issn = { 0975-8887 },
pages = { 46-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number12/30585-2019918885/ },
doi = { 10.5120/ijca2019918885 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:50:12.988701+05:30
%A CH. V. V. S. Srinivas
%A M. V. R. V. Prasad
%A M. Sirisha
%T Remote Sensing Image Segmentation using OTSU Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 12
%P 46-50
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, extraction of information from remote sensing images is an active topic of research. Feature extraction from an image is performed by image segmentation by dividing the image into distinct and self-seminar pixel groups. In remote sensing images, large quantity of texture information is present. So, it is difficult and time consuming process to segment objects from the background in remote sensing images. Many algorithms have been proposed for the purpose of segmentation of remote sensing images. Thresholding is a simple technique but effective method to separate objects from the background. A commonly used method, the Otsu method, improves the image segmentation effectively. It is the most referenced thresholding methods, as it directly operates on the gray level histogram. In this project, Otsu thresholding algorithm is used to segment the roads and residential areas from the vegetation areas in remote sensing images.

References
  1. F. Lang, J. Yang, D. Li, L. Zhao, and L. Shi, “Polarimetric SAR image segmentation using statistical region merging,” IEEE Geosci. Remote Sens. Lett., vol. 11, no. 2, pp. 509–513, Feb. 2014.
  2. L. Zhang, H. Li, P. Wang, and X. Yu, “Detection of regions of interest in a high-spatial-resolution remote sensing image based on an adaptive spatial subsampling visual attention model,” GISci. Remote Sens., vol. 50, no. 1, pp. 112–132, Feb. 2013.
  3. N. Otsu, “A threshold selection algorithm from gray-level histograms,” IEEE Trans. Syst. Man Cybern., vol. SMC-9, no. 1, pp. 62–66, Jan. 1979.
  4. Niblack W.: An Introduction to Digital Image Processing. Prentice Hall, Englewood Cliffs, NJ, USA (1986)
  5. Gonzalez, Rafael; Richard Woods. Digital Image Processing (3rd ed.)
  6. M. Sezgin and B. Sankur. “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic Imaging, pp.146-156, 2003.
  7. Belhumeur P N, Hespanha J P, Kriegman D J (1997) IEEE Transactions onPattern Analysis and Machine Intelligence 19:711–720.
  8. Jenks, George F. 1967. "The Data Model Concept in Statistical Mapping", International Yearbook of Cartography 7: 186–190.
Index Terms

Computer Science
Information Sciences

Keywords

Thresholding techniques Otsu method image segmentation optimal threshold selection range minimum variance ratio remote sensing.