A Modified Watershed Technique for Segmentation of
High Resolution Satellite Images
Li-Yu Chang and Chi-Farn Chen
Center for Space and Remote Sensing Research
National Central University
No.300, Jungda Rd, Jhongli City, Taoyuan, 320, TAIWAN
Tel: 886-3-4227151-57688, Fax: 886-3-4264301
E-mail: lychang@csrsr.ncu.edu.tw
ABSTRACT
Watershed segmentation is a widely used technique for image partitioning. However,
over-segmentation normally occurs when the smoothing process is not operated properly. For
example, improper selection of smoothing scheme and parameters will not only wipe out the
detail of image content but also affect the accuracy of following feature extraction. In order to
overcome these drawbacks, a modified watershed segmentation method based on the edge
intensity filtering is proposed. An experiment using both QuickBird multi-spectral and
panchromatic images is performed to test the proposed scheme. The result shows that the
proposed scheme can generate reasonable and simplified segmentation output for different type
of objects.
1. INTRODUCTION
Due to the recent progress of satellite imaging system, the image resolution of some commercial
satellites can reach to less than 1 meter in panchromatic mode. Such high spatial resolution
images certainly will provide lots of detail for remote sensing users. Nevertheless, the complex
image content will make it difficult to develop image algorithms for automated image feature
extraction and pattern recognition. Image segmentation is one of the useful techniques to extract
regions from satellite images. It is usually regarded as a preprocessing procedure for image
classification (Black, 1998). Among numerous segmentation techniques, watershed algorithm is
a sophisticated technique for extracting region features from images (Hagyard, 1996 and Perona,
1990). The basic concept of watershed algorithm is to perform watershed operation on an edge
image that is obtained from the source data. However, the complex texture of images usually
causes over-segmentation. To avoid such consequence, a smoothing filter must be used in
advance (Scheunders, 2001). Nevertheless, improper selection of smoothing filter or its
parameters will not only smear the detail of source data but also affect the edge accuracy of the
extracted feature. Therefore, the role of smoothing filter becomes a key factor in the whole
processing procedures. There are some smoothing algorithms which can reduce noise and
preserve accurate edge information simultaneously (Thomas, 1987 and Vincent, 1997).
However, these algorithms still need user to select a suitable windows size. Besides, the relation
between the scale of segmented object and the windows size of smoothing filter is highly
dependent and nontrivial.
A modified watershed segmentation algorithm is proposed to improve this weakness. Unlike the
traditional approach, we directly apply watershed segmentation to the edge image of input data
without smoothing, which apparently will cause over-segmentation. However, the most detail of
segment information is preserved. Then, an iterative patch merging procedure is applied to this
result to create multiple segmentation layers. At last, an edge intensity filtering technique is
applied to the multiple segmentation layers to generate final result.
