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1

Values or classes not present in the data are typically not displayed in an automatically generated legend. There are a couple of workarounds typically used to address this. Create a sample dataset containing all values/classes and use that to generate the legend, but keep it out of view in the map. Convert the automatically created legend to a graphic - ...


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I would discourage you from perusing the course that you are on. In most cases there is some degree of useful reflectance information in every pixel. Before applying a dubious technique that just excludes what is perceived as shadow, you could first attempt to correct for the effect. I have had consistent success with the Minnaert correction in very steep ...


3

NDVI is for vegetation/non vegetation discrimination. So if your vegetation is always coniferous forest, then it should be the most efficient method in your case. Otherwise you will have confusions with crop, grassland and deciduous forests. In a montainous area, single reflectance thresholds will be problematic due to the hillshade (clearly visible on ...


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To create a DHM subtract the DEM from the DEM, this can be done in Esri Raster Calculator or GDAL_CALC. This will put all your elevations on a 'level playing field'. Syntax (Substitute full paths for DEM, DSM & DHM): GDAL_CALC.py -A DSM -B DEM --outfile=DHM --CALC "A-B" The DHM will be mostly 0 (or near enough), which you make your nodata value. With ...


3

There is a considerable body of literature on individual crown detection in spectral and lidar data. Methods wise, perhaps start with: Falkowski, M.J., A.M.S. Smith, P.E. Gessler, A.T. Hudak, L.A. Vierling and J.S. Evans. (2008). The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. ...


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I am posting this as an answer due to length limit in comment, no hopes for credits:). Very broad brush, providing you've got DEM. Extract DEM for individual polygon to dem. Define dem's elevation extremes Set zCur+=-zStep. Step to be found by iterations beforehand, e.g. reasonable drop between 'tree top cell' elevation and neighbours Below=Con (dem => ...


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I think you can't... You first have to label each classes to compare them. Kmean classify unsupervisedly so without any prior information and so cannot define any kind of classes. If you have a reference layer, you can make a labelling by a majority voting. Here's a quite more efficient code for majority voting than using the 'raster' package function zonal ...


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Actually, it is not granted that you will be able to recover some information from the shadowed areas. However, I once dealt successfully with (cloud) shadows in a hyperspectral image. The aim was simple land cover classification. Here's what I did. I'm not sure how this would work with Landsat images, but since it is very simple you should give it a try. ...


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Remove the outline of the feature style in Layer Properties | Style or turn it white - depending on which effect you prefer.


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This issue has been resolved. The problem was an invalid character in the file path name creating a error message and not creating the signature(.gsg) file.


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I think that somewhere in the classification process you are including spatial coordinates or pixel row/column IDs of your training samples. For a purely spectral classification and classes distributed in a spatially homogeneous manner it is not required to include spatial coordinates. From a random forest perspective, this would explain the linear ...


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A classifier, any classifier, can classify any kind of data. These objects, as Aaron correctly states, can be pixels, objects, superpixels, bananas, sounds, DNA, etc. The main differences which, in my opinion, is really relevant between superpixel- and pixel-based classification are as follows: pixel based : The resolution of the prediction is maximal, ...



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