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Distributed texture-based land cover classification algorithm using hidden Markov model for multispectral data.
- Source :
-
Survey Review . Nov2016, Vol. 48 Issue 351, p430-437. 8p. - Publication Year :
- 2016
-
Abstract
- Land cover classification is a vital application area in the satellite image processing domain. Texture is a useful feature in land cover classification. In this paper, we propose a distributed texture-based land cover classification algorithm using Hidden Markov Model (HMM). Here, HMM is used for texture-based classification of remotely sensed images. Furthermore, to enhance the performance, data-intensive remotely sensed image is segmented and distributed into parallel sessions. Experiments were conducted on IRS P6 LISS-IV data, and the results were evaluated based on the confusion matrix, classification accuracy, and Kappa statistics. These results indicate that the proposed algorithm achieves a classification accuracy of 88.75%. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00396265
- Volume :
- 48
- Issue :
- 351
- Database :
- Academic Search Index
- Journal :
- Survey Review
- Publication Type :
- Academic Journal
- Accession number :
- 119208686
- Full Text :
- https://doi.org/10.1179/1752270615Y.0000000041