Submit Manuscript

Research Journal of Forestry

Year: 2016 | Volume: 10 | Issue: 1 | Page No.: 23-29
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

Article Trend

Total views 422

Authors

Masoumeh Khedmatgozar Dolati

Amir EslamBonyad

Keywords

Research Article

Use of Principal Component Analysis in Accuracy of Classification Maps (Case Study: North of Iran)

Masoumeh Khedmatgozar Dolati and Amir EslamBonyad
Objective: The main objective of this study was to investigate the role of principal component analysis to improve the accuracy of classification. Methodology: In the present study, PCA has been successfully applied in IRS images of north of Iran (Shafaroud) showing that the first principal components contain more variance of the information in the original four bands. Results: Classification was performed on two sets of data. In the first one, original bands of LISS III (b2-b3-b4-b5) and in the second classification, original bands of LISS III in combination with first component (pca1) was used. Classification was performed with five classes including sea, agriculture, settlements, broad leaf forest and needle leaf forest. In (b2-b3-b4-b5) and b2-b3-b4-b5 in composition with pca1, obtained overall accuracy and kappa coefficient were 99.27-98.94 and 99.37-99.09, respectively. Conclusion: The obtained results indicate that overall accuracy and kappa coefficient increases when pca 1 used along with main bands.
PDF Fulltext XML References Citation

How to cite this article

Masoumeh Khedmatgozar Dolati and Amir EslamBonyad, 2016. Use of Principal Component Analysis in Accuracy of Classification Maps (Case Study: North of Iran). Research Journal of Forestry, 10: 23-29.

URL: https://scialert.net/abstract/?doi=rjf.2016.23.29

Leave a Comment

Your email address will not be published. Required fields are marked *