Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms
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 sensi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2009-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2009/601638 |
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| author | K. Parvathi B. S. Prakasa Rao M. Mariya Das T. V. Rao |
| author_facet | K. Parvathi B. S. Prakasa Rao M. Mariya Das T. V. Rao |
| author_sort | K. Parvathi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1b642e53bf44486ea5c7a8297259121f |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2009-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-1b642e53bf44486ea5c7a8297259121f2025-02-03T01:23:31ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2009-01-01200910.1155/2009/601638601638Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet TransformsK. Parvathi0B. S. Prakasa Rao1M. Mariya Das2T. V. Rao3Department of Electronics and Communication Engineering (ECE), Jagannath Institute for Technology and Management (JITM), Parlakhemundi, Gajapati 761211, Orissa, IndiaGandi Institute of Technology and Management, Pinagadi, Visakhapatnam 531173, Andhra Pradesh, IndiaDepartment of Instrument Technology, Andhra University, Visakhapatnam 530003, Andhra Pradesh, IndiaDepartment of Geo-Engineering, Andhra University, Visakhapatnam 530003, Andhra Pradesh, IndiaThe 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.http://dx.doi.org/10.1155/2009/601638 |
| spellingShingle | K. Parvathi B. S. Prakasa Rao M. Mariya Das T. V. Rao Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms Discrete Dynamics in Nature and Society |
| title | Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms |
| title_full | Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms |
| title_fullStr | Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms |
| title_full_unstemmed | Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms |
| title_short | Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms |
| title_sort | pyramidal watershed segmentation algorithm for high resolution remote sensing images using discrete wavelet transforms |
| url | http://dx.doi.org/10.1155/2009/601638 |
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