Watershed Module theory: Difference between revisions
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In the study of image processing, a watershed<ref>https://en.wikipedia.org/wiki/Watershed_%28image_processing%29</ref> is a transformation defined on a grayscale image. Some of the proposed algorithms use the principle of flooding water on a DEM to identify individual watersheds, separated by borders of higher altitude. | In the study of image processing, a watershed<ref>https://en.wikipedia.org/wiki/Watershed_%28image_processing%29</ref> is a transformation defined on a grayscale image. Some of the proposed algorithms use the principle of flooding water on a DEM to identify individual watersheds, separated by borders of higher altitude. | ||
One of the problems that often occur when generated watersheds is over-segmentation. Over-segmentation occurs when the elevation data contains many local | One of the problems that often occur when generated watersheds is over-segmentation. Over-segmentation occurs when the elevation data contains many local minima. To overcome this issue, the elevation data is often smoothed to remove local minima and generate larger watersheds. | ||
Our watershed algorithm is used for a slightly different purpose, namely identifying the watershed areas for predefined waterways. More generally, one is interested into which waterway a set of rain drops will end up based on the terrain elevation and the starting location of the drops. | Our watershed algorithm is used for a slightly different purpose, namely identifying the watershed areas for predefined waterways. More generally, one is interested into which waterway a set of rain drops will end up based on the terrain elevation and the starting location of the drops. | ||
Revision as of 14:16, 20 May 2020
In the study of image processing, a watershed[1] is a transformation defined on a grayscale image. Some of the proposed algorithms use the principle of flooding water on a DEM to identify individual watersheds, separated by borders of higher altitude.
One of the problems that often occur when generated watersheds is over-segmentation. Over-segmentation occurs when the elevation data contains many local minima. To overcome this issue, the elevation data is often smoothed to remove local minima and generate larger watersheds.
Our watershed algorithm is used for a slightly different purpose, namely identifying the watershed areas for predefined waterways. More generally, one is interested into which waterway a set of rain drops will end up based on the terrain elevation and the starting location of the drops.
Since we support the calculation of rainfall using our water module and can generate water flow direction results, we have altered the watershed algorithm to use the flow direction instead of the terrain elevation as input. Given that the rainfall used is adequately large, it helps us reducing the amount of local minima and hopefully generate more realistic results based on rainfall simulation.
Topics
- Watershed algorithm, for the used algorithm in the Watershed Module.