Multi-dimensional edge detection operators

Sungwook Youn, Chulhee Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


In remote sensing, modern sensors produce multi-dimensional images. For example, hyperspectral images contain hundreds of spectral images. In many image processing applications, segmentation is an important step. Traditionally, most image segmentation and edge detection methods have been developed for one-dimensional images. For multidimensional images, the output images of spectral band images are typically combined under certain rules or using decision fusions. In this paper, we proposed a new edge detection algorithm for multi-dimensional images using secondorder statistics. First, we reduce the dimension of input images using the principal component analysis. Then we applied multi-dimensional edge detection operators that utilize second-order statistics. Experimental results show promising results compared to conventional one-dimensional edge detectors such as Sobel filter.

Original languageEnglish
Title of host publicationSatellite Data Compression, Communications, and Processing X
ISBN (Print)9781628410617
Publication statusPublished - 2014
EventSatellite Data Compression, Communications, and Processing X - Baltimore, MD, United States
Duration: 2014 May 82014 May 9

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherSatellite Data Compression, Communications, and Processing X
Country/TerritoryUnited States
CityBaltimore, MD

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Multi-dimensional edge detection operators'. Together they form a unique fingerprint.

Cite this