Details

Evaluation of Image Texture Parameters for Urban Land Cover Classification

J Jacinth Jennifer

PG student, Department of Civil Engineering, Anna University Regional Campus Tirunelveli, India

S Vanmathy

PG student, Department of Civil Engineering, Anna University Regional Campus Tirunelveli, India

C Maria Jobi Sahana

PG student, Department of Civil Engineering, Anna University Regional Campus Tirunelveli, India

G Devi

Assistant Professor, Department of Civil Engineering Anna University Regional Campus Tirunelveli, India

183-190

Vol: 6, Issue: 3, 2016

Receiving Date: 2016-06-14 Acceptance Date:

2016-07-11

Publication Date:

2016-08-31

Download PDF

Abstract

Remote sensing technology is paving its own way with remarkable advantages in various fields. Feature extraction from satellite imagery is one of the challenging tasks in image processing. As far as low resolution images were concerned, per pixel analysis and sub-pixel analysis were gaining its importance. Now with the advancements in technology, high resolution imageries are acquired easily and made used in various applications. When speaking about high resolution imageries, it is made known that an object or feature in the imagery is made up of several pixels which is contrary to the low resolution imageries. Thus, an alternative technique for feature retrieval is necessary to extract features from high resolution imagery. Spatial relationships between the pixels were taken into consideration along with its spectral characteristics. Texture is one of the spatial parameter which is of much importance. The texture of an image gives us the information about the spatial arrangement of colours or intensities and it is a function of the texture surface, its albedo, the illumination and the camera and its viewing position. There are various parameters to characterize the texture of an image. Haralick’s texture parameters were found to be of much importance compared to the other texture parameters. Thus Haralick’s texture parameters were considered in the study. There are about thirteen Haralick’s texture parameters. In urban feature extraction, it is not necessary that all these thirteen parameters have to be imposed because certain parameters have no influence in extracting the urban features. So based on this aspect, statistical analysis was made so as to examine and quantify the influence of each Haralick’s texture parameter. Out of thirteen, six were found to be of considerable importance. Using these six textural characteristics, classification was carried out and 88% accuracy was obtained in urban feature extraction.

Keywords: texture; satellite imagery; haralick’s texture parameters; statistical analysis; urban feature extraction; classification

References

  1. Robert M. Haralick, K.Shanmugam and ITS‟HAK Dinstein (1973), “Textural Features for Image Classification”, IEEE Transactions on Systems, Man and Cybernetics, Vol.3, No.6, pp.610-621.
  2. Manik Varma and Andrew Zisserman (2004), “A Statistical Approach to Texture Classification from Single Images”, Robotics Research Group, University of Oxford, Oxford, UK.
  3. Jitendra Malik, Serge Belongie, Thomas Leung and Jianbo Shi (2001), “Contour and Texture Analysis for Image Segmentation”, International Journal of Computer Vision, Vol.43, No.1, pp.7-27
  4. Kiranmayee.M and Subbarao.M (2012), “Texture Classification using Weighted Probablistic Neural Networks”, International Journal of Image Processing and Vision Sciences, Vol.1, Issue.2, pp.38-40
  5. Paidamwoyo Mhangara and John Odindi (2013), „Potential of Texture-based Classification in Urban Landscapes using Multispectral Aerial Photos‟, South African Journal of Science, Vol.109, No.3, pp.1-8.
  6. Rellier G., Descombes X., Falzon F. and Zerubia J. (2004), „Texture Feature Analysis using a Gauss-Markov model in Hyperspectral Image Classification‟, Geoscience and Remote Sensing, IEEE Transactions, Vol.42, No.7, pp.1543-1551.
  7. Shervan Fekri Ershad (2011), „Colour Texture Classification Approach based on Combination of Primitive Pattern Units and Statistical Features‟, The International Journal of Multimedia and its Applications (IJMA), Vol.3, No.3.
  8. Xiuwen Liu and Deliang Wang (2003), „Texture Classification using Spectral Histograms‟, IEEE Transactions on Image Processing, Vol.12, No.6, pp.661-670.
  9. Yun Zhang (2001), „Texture-Integrated Classification of Urban Treed Areas in High-Resolution Colour- Infrared Imagery‟, Photogrammetric Engineering and Remote Sensing, Vol.67, No.12, pp.1359-1365.
  10. Shervan Fekri Ershad (2011), „Texture Classification Approach based on Combination of Edge and Co-occurance and local Binary Pattern‟, International Conference IP, Computer Vision and Pattern Recognition (IPCV ’11), pp.626-629.
Back

Disclaimer: All papers published in IJRST will be indexed on Google Search Engine as per their policy.

We are one of the best in the field of watches and we take care of the needs of our customers and produce replica watches of very good quality as per their demands.