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The two ways which usually works quite well for me are Histogram graphs which you can create from View menu > Graphs. Tutorial on that. Statistics window which can be opened from the attribute table. Right-click the column name and choose Statistics. If you want to create a more professional histogram, you could use external libraries such as Python ...


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In order to get at the classes you describe, you will need to incorporate a sophisticated classification algorithm and ancillary data derived from the imagery. I would recommend two approaches: 1) an object-oriented image segmentation (IS) approach using IS software such as eCognition or 2) a pixel-based non-metric, decision tree (Random Forest) approach ...


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You need to make sure that names(sdata@data[,3:ncol(sdata@data)]) and names(xvars) are exactly the same. Check this using identical(names(sdata@data[,3:ncol(sdata@data)]), names(xvars)) If TRUE, your predict should run fine. The edit related warnings/errors are irrelevant, they relate to you trying to display a SpatialPolygonsDataFrame (and S4 class ...


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The short answer is: all of them. Most classification algorithms can derive useful information from all of the bands. However, there are certainly bands that are better at discriminating between vegetation types. You may also want to incorporate band indices such as NDVI or EVI into your classification algorithm. NASA produced some useful tables to help ...


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Under Properties > Styles there is a Color interpolation setting that can be set to Discrete, giving your raster map a more kind of vector polygon look.


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Your script is configured to take a point shapefile of training data and use that to train a Random Forest classifier. The screenshot shows the form the shapefile attribute table needs to take in order to be used as a training set. The important fields here are 1) the XY coords, 2) the pixel values for each band at that XY coordinate, and 3) the class of ...


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Answered as How to perform Raster Reclassification in QGIS? indirectly, but I found it can be done either using the Con (Spatial Analyst) tool (@Aaron), or better using the Reclassify (Spatial Analyst) tool. The "Classify..." button provides the same functionality as in the Layer Symbology dialogue box. Reclassify creates a new raster based on the ...


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I am interpreting your question to ask if you can generate training data from 1 image and apply those statistics to the other 4 images by normalizing the other 4 to the first. This seems dubious to me. While it is possible to normalize atmospheric differences, there are other differences (e.g. phenology, recent weather events) that make normalizing ...


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A quick Google search threw up these papers, I am sure there are? http://edcintl.cr.usgs.gov/ip/macga/docs/Helmer.Ruef.2007_CJRS_33.pdf http://www.abdn.ac.uk/geospatial/ers/PAPERS/radiometric.pdf



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