A Textural Approach for Land Cover Classification of Remotely Sensed Image

Texture features play a vital role in land cover classification of remotely sensed images. Local binary pattern (LBP) is a texture model that has been widely used in many applications. Many variants of LBP have also been proposed. Most of these texture models use only two or three discrete output le...

Full description

Saved in:
Bibliographic Details
Published in:International journal of computer applications 2014-01, Vol.92 (10), p.47-52
Main Authors: Jenicka, S, Suruliandi, A
Format: Article
Language:English
Subjects:
Boundaries
Classification
Fuzzy
Land cover
Remote sensing
Support vector machines
Surface layer
Texture
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Texture features play a vital role in land cover classification of remotely sensed images. Local binary pattern (LBP) is a texture model that has been widely used in many applications. Many variants of LBP have also been proposed. Most of these texture models use only two or three discrete output levels for pattern characterization. In the case of remotely sensed images, texture models should be capable of capturing and discriminating even minute pattern differences. So a multivariate texture model is proposed with four discrete output levels for effective classification of land covers. Remotely sensed images have fuzzy land covers and boundaries. Support Vector Machine (SVM) is highly suitable for classification of remotely sensed images due to its inherent fuzziness. It can be used for accurate classification of pixels falling on the fuzzy boundary of separation of classes. Hence in this article, land cover classification of remotely sensed image has been performed using the proposed multivariate texture model MDLTP (Multivariate Discrete Local Texture Pattern) and SVM classifier. The classification accuracy of the classified image obtained is found to be 93. 46%.
ISSN:0975-8887
0975-8887
DOI:10.5120/16049-5370