How-to FMMidwater - Feature Detection Tool
The Feature Detection plugin in the FMMidwater (FMMW) module uses a set of algorithms to extract points of interest from watercolumn data. In the Fledermaus 7.4.1 release use of this tool is limited to Kongsberg data; the feature detection plugin refracts points for cluster analysis on export, and refraction in FMMidwater is only fully implemented for Kongsberg solutions. A flat bottom assumption and proper refraction for other systems will be added in subsequent releases, which will allow use of this tool on any watercolumn format support by FMMidwater. This howto will be updated to reflect those change.
Data used in this howto courtesy of the University of New Hampshire Center for Coastal and Ocean Mapping / Joint Hydrographic Center and NOAA Office of Exploration and Research.
- Open your FMMidwater project, or create a new project. If you haven't done so already, use File > Add Sonar Files to add the file containing watercolumn data; use File > Add Navigation to add the navigation information if it is separate from the watercolumn information.
Convert the data file(s) to GWC (Generic Water Column) format by going to Tools > Convert Sonar Data. Note that if you choose to down-sample the data on conversion, you will have less points for your analysis. Generally most systems do not need to be down-sampled.
- If you have multiple files loaded and converted, select the GWC file you are interested in viewing first. The GWC files are listed under the Midwater Node in the Source Files window.
- Examine the data in Stacked View to get an overview of the line and what is present in the watercolumn.
- Go to Tools > QPS > Feature Detection. The Seep Hunter tool will launch; initially no data will be loaded.
- Click Sync in the right left corner of the Seep Hunter window. The normalization and despeckling algorithms will run on the selected GWC file and the normalized/despeckled data will load.
- The Despeckled view is where potential features will be visible. To zoom in on a potential feature, left click and drag a box around the area of interest. To zoom out, click the Zoom Out or Reset View buttons in the top right corner.
- The Normalization and Despeckle filter options can be adjusted using the Settings buttons; after adjusting any parameters click one of the Re-Run buttons to re-run the filters. The icons used for the button are not indicative of their functionality; hover your mouse over the button and a tip describing it will appear.
- When you are happy with the results, use the Save button to save the data visible in your Despeckled window. The file will save to your Project > Output > SD folder; you will have the option to save as a 3D Point Object for Cluster Analysis, a basic 3D Point Object, or as an ASCII dump of points. The output coordinate system for the file will default to the UTM Zone that matches your data; however you can choose any output coordinate system you prefer, including an unprojected "geographic" coordinate system such as WGS 84 or NAD 83. If you plan to export a 3D Point Object for Cluster Analysis, you should set your output projection to a projected coordinate system.
- The first two options are Fledermaus SD files that can be directly loaded into the Fledermaus module for visualization and further analysis. The Basic 3D Point Object is a standard Fledermaus Point SD for visualization; the 3D Point Object for Cluster Analysis is a special Fledermaus Point SD (Point Clusters SD) that has additional tools specifically for performing clustering and characterizing seeps. For more information on the Point Clusters SD object type, see this Howto.
The Ascii Dump option will make a text file with x, y, z, and amplitude. This file can be imported into Fledermaus as points, however this export is typically done when the user needs a common file format to use in other software.
- Open the Fledermaus module. Load exported SD files in Fledermaus with supporting data for visualization and further analysis.
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