Hello,

I have a high resolution drone mosaic image (RGB only) showing a small constructed cattail marsh bordered by upland meadow species.  This covers a small area, maybe 75x75m.   The cattail is very distinct in this image relative to the upland meadow.  I'm experimenting with approaches to have ecognition recognize this cattail marsh relative to the meadow.  Given that the textural difference is visually distinct, I would think I'd be able to apply some type of textural analysis.  I see the Harlick texture options, but I have no idea how to use these.  Other than doing an initial segmentation, do I need to do something prior to using those algorithms?  If not, what would be my approach?  

Thanks

3 comments

  • Keith Peterson

    The Haralick texture tools are object features and require image objects in order to calculate. Therefore, you will first need to apply a segmentation to your data. Then you can create/calculate the Haralick features as needed.
    But, I would recommend trying a simple standard deviation feature, or calculate the layer from your data, first. This is also a very good indicator of texture and much faster to compute.

  • James Leslie

    Thanks. I do recognize that segmentation is needed first, but after that's complete, how do I apply a Haralick texture tool to that segmentation? Do I add a new process/algorithm? Sorry, would be helpful if you could assume I know very little about eCognition... because that would be true. Same questions apply to your recommendation about using a simple standard deviation, or calculating the layer from my data. Thanks

  • Christian Weise

    Hello James,

    Haralick or StdDev are object features and always available as soon as object are generated. Please click on an object and take a look into the Object Information window (here you can see for one selected object many object features) or double click on a features (like a Haralick) in the "Feature View" so that you can see the selected feature for all available objects in the Data View.

    Cheers
    Christian

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