Abstract
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.
Highlights
Image segmentation is object oriented and useful in high-resolution image analysis
Tracking of intensity extrema along scales defined by Lifshitz 1 and Lindeberg 2 was used for image segmentation
Other approach for image segmentation is based on the watershed transform
Summary
Image segmentation is object oriented and useful in high-resolution image analysis. It provides a partitioning of the image into isolated regions, each one representing a different image. Other approach for image segmentation is based on the watershed transform This transform can be applied to the gradient magnitude image as defined by Meyer and Beucher 3 , Vincent and Soille 4 , and Bieniek and Moga 5 to obtain the segmented regions. Small fluctuations in the grey levels produce spurious gradients, which cause over segmentation To overcome this problem many techniques based on watersheds have been proposed. Meyer 6 introduced the leveling approach, which applies morphological filters to reduce the small details in the image. J. Kim 13, proposed a wavelet-based watershed segmentation technique, by projecting the segmented image into higher resolutions. Almost all the proposed techniques are applied on the medical and nonmedical images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
