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A Textural Approach for Land Cover Classification of Remotely Sensed Image
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 92 - Number 10 |
| Year of Publication: 2014 |
| Authors: S. Jenicka, A. Suruliandi |
10.5120/16049-5370
|
S. Jenicka, A. Suruliandi . A Textural Approach for Land Cover Classification of Remotely Sensed Image. International Journal of Computer Applications. 92, 10 ( April 2014), 47-52. DOI=10.5120/16049-5370
Abstract
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%.
References
- Ojala, Pietikainen and Maenpaa, 2001. A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification, Advances in Pattern recognition. ICAPR, Vol. 2013, pp 399-408.
- Lucieer, Stein and Fisher, 2005. Multivariate Texture-based Segmentation of Remotely Sensed Imagery for Extraction of Objects and Their Uncertainty, International Journal of Remote Sensing. vol. 26 , no. 14, pp. 2917-2936.
- Arivazhagan,. S, and. Ganesan,. L, 2003. Texture segmentation using wavelet transform, Pattern Recognition Letters, Elsevier, vol. 24, Issue 16, pp. 3197–3203.
- Arivazhagan,. S, Ganesan,. L, Priyal,. S,2006. Texture classification using Gabor wavelets based rotation invariant feature, Pattern Recognition Letters, December, vol. 27, Issue 16, pp. 1976-1982.
- Liao and Chung,. C. S. , . 2007. Texture Classification by Using Advanced Local Binary Patterns and Spatial Distribution of Dominant Patterns, Acoustics, Speech and Signal Processing, IEEE International Conference ICASSP.
- Surliandi,. A, and Ramar,. K,, 2008. Local Texture Patterns- A Univariate Texture Model for classification of images. ADCOM 2008, pp. 14-17.
- Dimitris K. Iakovidis, Eystratios G. Keramidas, and Dimitris Maroulis. 2008. Fuzzy Local Binary Patterns for Ultrasound Texture Characterization, ICIAR 2008, LNCS 5112, pp. 750–759.
- Suruliandi,. A. , 2009. A study on classification of remotely sensed multispectral mages-a textural approach. Ph. D dissertation work submitted to Manonmaniam Sundaranar University", Tamil Nadu, India.
- Liao, . S, . Law, . W. K, Chung, . C. S. , . 2009. Dominant Local Binary Patterns for texture classification. , IEEE transaction on Image Processing, vol 18, no 5.
- Raju, U. S. N. , Sridhar Kumar,. A, Mahesh,. B, and Reddy, . E, 2010. "Texture Classification With High Order Local Pattern Descriptor: Local Derivative Pattern", Global Journal of computer Science and Technology, vol. 10, Issue 8, Ver. 1. 0.
- Srinivasan, . E. M, Ramar. K, and Suruliandi,. A, . 2011. Texture Analysis Using Local Texture Patterns: a Fuzzy Logic Approach", International Journal of Pattern Recognition and Artificial Intelligence, pp. 741-762, vol 25, no 5.
- Jae Young Choi, Yong Man Ro, and Konstantinos, N. Plataniotis . 2012. Color local texture features for color face recognition, IEEE transactions on image processing. vol 21, no. 3.
- Seung Ho Lee, Jae Young Choi, Yong Man Ro, and Konstantinos. N. Plataniotis, . 2012. Local color vector binary patterns from multichannel face images for face recognition. IEEE transactions on image processing, vol 21, no. 4.
- Suruliandi, . A, and Jenicka,. S. , . 2014. Texture based classification of remotely sensed images, International journal of signal imaging and systems engineering, Inderscience publisher, Accepted for publication in Sep. 2013, Abstract available online.
- Hermes, Frieauff, and Puzicha, . 1999. Support Vector Machines for Land Usage Classification in Landsat TM imagery, Proc. IEEE Geoscience and Remote Sensing Society, vol. 1, pp. 348-350.
- Qiu Zhen Ge, Ling, and Qiong . 2008. High Efficient classification on SVM , International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII. Part B2, Beijing.
- Demir, and Ertuirk . 2007. "Hyperspectral image classification using Relevence Vector Machines" , IEEE Geoscience and remote sensing letters, vol. 4, no. 4. Lu, . D, and Weng,. Q, . 2007. A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, Taylor & Francis, vol 28, no 5, pp. 823-870.
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