CFP last date
20 June 2024
Reseach Article

Classification of Land Cover in Satellite Image using Supervised and Unsupervised Techniques

by Balamurugan G., K. B. Jayarraman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 135 - Number 5
Year of Publication: 2016
Authors: Balamurugan G., K. B. Jayarraman
10.5120/ijca2016908382

Balamurugan G., K. B. Jayarraman . Classification of Land Cover in Satellite Image using Supervised and Unsupervised Techniques. International Journal of Computer Applications. 135, 5 ( February 2016), 15-18. DOI=10.5120/ijca2016908382

@article{ 10.5120/ijca2016908382,
author = { Balamurugan G., K. B. Jayarraman },
title = { Classification of Land Cover in Satellite Image using Supervised and Unsupervised Techniques },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 135 },
number = { 5 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 15-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume135/number5/24045-2016908382/ },
doi = { 10.5120/ijca2016908382 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:34:55.426902+05:30
%A Balamurugan G.
%A K. B. Jayarraman
%T Classification of Land Cover in Satellite Image using Supervised and Unsupervised Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 135
%N 5
%P 15-18
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Remote Sensing plays a vital role for the detection of urban expansion. Due to high complexity of urban landscapes such as building area, vegetation area are classified based on the feature extraction from the satellite Images. Different feature Extraction methods are employed for obtaining the primitives such as texture, shapes and sizes etc. In this paper, obtaining first order statistics, GLCM and Wavelet transformation for the feature extraction and then final classification is processed using proposed supervised and unsupervised Technique for the urban landscape classification.

References
  1. D. Menaka, L. Padma Suresh and S.Selvin Premkumar (2015) Wavelet Transform-Based Land Cover Classification of Satellite Images, Artificial Intelligence and Evolutionary Algorithms in Engineering Systems and Computing.
  2. S.Zang, Q.Zhou, (2012) New feature extraction algorithm for satellite image-non-linear small objects, in IEEE Symposium on Electrical and Electronics Engineering
  3. S.Arivazhagan, L.Ganesan (2003) Texture classification using wavelet Transform, Pattern recognition.
  4. A.Baradi, F. Parmiggiani, An investigation of the textural characteristics associated with gray level co occurrence matrix statistical parameters, IEEE Trans.Geosci.Remote Sens.33 (2)
  5. B. Sırmaçek, C. Ünsalan, A probabilistic framework to detect buildings in aerial and satellite images. IEEE Trans. Geosci. Remote Sens. 49(1), 211–221 (2011)
  6. B. Tian, M.R. Azimi-Sadjadi, T.H. Vonder Haar, D. Reinke, Temporal Updating Scheme for Probabilistic Neural Network with Application to Satellite Cloud Classification. IEEE Trans.Neural Networks 11(4), 903–920 (2000)
  7. L.A. Ruiz, A. Fdez-Sarría, J.A. Recio, Texture feature extraction for classification of remote sensing data using wavelet decomposition: a comp study. Remote Sens. Spatial Inf. Sci., in Proceedings of International Archives Photogramm (2004), pp. 1109–1115
Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction First order statistics GLCM Wavelet transformation supervised and unsupervised Technique