CFP last date
22 July 2024
Reseach Article

Land Information Extraction with Boundary Preservation for High Resolution Satellite Image

by Suresh Singh, Merugu Suresh, K. Jain
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 7
Year of Publication: 2015
Authors: Suresh Singh, Merugu Suresh, K. Jain
10.5120/21243-4014

Suresh Singh, Merugu Suresh, K. Jain . Land Information Extraction with Boundary Preservation for High Resolution Satellite Image. International Journal of Computer Applications. 120, 7 ( June 2015), 39-43. DOI=10.5120/21243-4014

@article{ 10.5120/21243-4014,
author = { Suresh Singh, Merugu Suresh, K. Jain },
title = { Land Information Extraction with Boundary Preservation for High Resolution Satellite Image },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 7 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number7/21243-4014/ },
doi = { 10.5120/21243-4014 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:05:38.997211+05:30
%A Suresh Singh
%A Merugu Suresh
%A K. Jain
%T Land Information Extraction with Boundary Preservation for High Resolution Satellite Image
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 7
%P 39-43
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The advancement of technology in satellite system has drastically improved the quality of images which we call high resolution images. Today we have many satellites which provide high resolution images such as QUICKBIRD, IKONOS,WORLD-VIEW etc. High resolution provides much greater detail of information such as buildings or trees etc. can be seen clearly. Now the question arises how we can extract these land objects which contain various information. Traditionally we use manual digitization which is a time taking task and not appropriate for the changing land details. In this modern world we need some fast techniques which can extract the land boundaries as well as give the information associated with them such as their area. Object based techniques are used for the high resolution images but it is associated with the problem of proper segmentation. This paper includes efficient technique for edge detection to define land boundaries and feature selection technique for land information extraction. So this paper aims to use an edge detection technique and object based classification to extract the land information automatically and then associate the area detail with each land object.

References
  1. Anuj Tiwari, Merugu Suresh, Arun Kumar Rai, (2014), Ecological Planning for Sustainable Development with a Green Technology: GIS, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 3 Issue 3, March 2014, ISSN: 2278 – 1323, pp 636-641.
  2. Arora S. Acharya J, Verma A, Panigrahi Prasanta K. (2007), Multilevel thresholding for image segmentation through a fast statistical recursive algorithm, Indian Institute of Technology, Kharagpur 721 302, Indiac Physical Research Laboratory, Navrangpura, Ahmedabad 380 009, India.
  3. Belgiu Mariana, Lampoltshammer Thomas J. (2013),Ontology based interpretation of very high resolution imageries grounding ontology on visual interpretation keys, Salzburg Department of Geoinformatics Schillerstrasse 30, 5020 Salzburg, Austria.
  4. Bouziani Mourad, Goita Kalifa (2010), Rule-Based Classification of a High Resolution Image in an Urban Environment Using Multispectral Segmentation Guided by Cartographic Data, IEEE transactions on geoscience and remote sensing, vol. 48, no. 8.
  5. Cleve Casey, Kelly Maggi, Kearns Faith R. , Moritz Max (2007), Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography, Center for Fire Research and Outreach, University of California, Berkeley, 137 Mulford Hall #3114, Berkeley, CA 94720, United States.
  6. Caetano Mario (2009), Image classification, Department of Geology,University of Prague,Czech Republic.
  7. G. T. Shrivakshan (September 2012), A Comparison of various Edge Detection Techniques used in Image Processing, Bharathiar University, Coimbatore, Tamilnadu, India, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 1.
  8. Jabari Shabnam and Zhang Yun (2013), Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems, Department of Geodesy and Geomatics Engineering, University of New Brunswick, 15 Dineen Drive, Fredericton, NB E3B 5A3, Canada.
  9. Karlsson Anders (2003),Classifcation of high resolution satellite images, Chalmers University of Technology SE-412 96 GAoteborg, Sweden.
  10. Lu D. and Weng Q. (2006), A survey of image classification methods and techniques for improving classification performance, Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809, USA.
  11. Merugu Suresh, Kamal Jain, (2014), A Review of Some Information Extraction Methods, Techniques and their Limitations for Hyperspectral Dataset International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 3 Issue 3, March 2014, ISSN: 2278 – 1323, pp 2394-2400.
  12. Merugu Suresh, Kamal Jain, (2013) Sub Pixel Analysis on Hypothetical Image by using Colorimetry, International Journal of Recent Technology and Engineering(IJRTE), ISSN: 2277-3878,Volume 2, Issue-4, September 2013.
  13. Merugu Suresh, Kamal Jain, (2013), Colorimetrically Resolution Enhancement Method for Satellite Imagery to Improve Land Use, 14th ESRI User Conference id: UCP0046, New Delhi, India, 11-12th Dec, 2013.
  14. Merugu Suresh, Kamal Jain, (2014), A New Super Resolution Mapping Algorithm by Combining Pixel and Subpixel-Level Spatial Dependences with Colorimetry, Journal of Indian Society of Remote Sensing (ISRS), 10/2014; 42(4):10, Springer.
  15. Narkhede H. P. (2013), Review of Image Segmentation Techniques, International Journal of Science and Modern Engineering (IJISME) ISSN: 2319-6386, Volume-1, Issue-8.
  16. Syed Sohel, Dare Paul, Jones Simon (2003), Automatic classification of land cover features with high resolution imagery and lidar data: an object-oriented approach, The university of California,USA.
  17. Vala Hetal J. , Baxi Astha (February 2013), A Review on Otsu Image Segmentation Algorithm, Department of Computer Engineering Parul Institute of Engineering & Technology, Waghodia, Gujarat (India). International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 2, Issue 2.
Index Terms

Computer Science
Information Sciences

Keywords

Land Parcel Edge Detection Object Feature Selection Segmentation