Discrete Dynamics in Nature and Society
Volume 2009 (2009), Article ID 601638, 11 pages
Research Article

Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

1Department of Electronics and Communication Engineering (ECE), Jagannath Institute for Technology and Management (JITM), Parlakhemundi, Gajapati 761211, Orissa, India
2Gandi Institute of Technology and Management, Pinagadi, Visakhapatnam 531173, Andhra Pradesh, India
3Department of Instrument Technology, Andhra University, Visakhapatnam 530003, Andhra Pradesh, India
4Department of Geo-Engineering, Andhra University, Visakhapatnam 530003, Andhra Pradesh, India

Received 10 December 2008; Revised 12 May 2009; Accepted 29 June 2009

Academic Editor: B. Sagar

Copyright © 2009 K. Parvathi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal) and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.