A Review on Segmentation Techniques in Large-Scale Remote Sensing Images

Authors

  • Rakesh Tripathi M.Tech Scholar TIEIT, Bhopal, M.P, India
  • Neelesh Gupta Professor, TIEIT, Bhopal, M.P, India

DOI:

https://doi.org/10.24113/ijoscience.v4i4.143

Abstract

Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.

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Published

04/20/2018

How to Cite

Tripathi, R., & Gupta, N. (2018). A Review on Segmentation Techniques in Large-Scale Remote Sensing Images. SMART MOVES JOURNAL IJOSCIENCE, 4(4), 31–36. https://doi.org/10.24113/ijoscience.v4i4.143