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7

Your fire hydrants will have a very unique spectral signature, therefore I would use supervised maximum likelihood classification to classify your raster. An alternative is to run an ISODATA algorithm for an unsupervised approach. Try the following (partial) workflow: Open Iso Cluster Unsupervised Classification in ArcGIS Enter ALL 3 bands (i.e. R, G, B) ...


5

There is no specific GIS software for doing this: most will handle the RGB image and the Lidar data. Basically, NDVI is (NIR - RED)/(NIR + RED). Most of the time, aerial Lidar gives you the NIR value (to be checked in metadata) and the first band of your RGB image gives you the RED value. Just make sure that your data are calibrated to reflectance (or, if ...


4

Vegetation extraction is a bit more complex than running the spatial analysis tools that you named. For better results I would suggest the following: run analysis on a 4 band image (e.g. R,G,B,NIR) change image to be symbolized as 432 for RGB not 321 create training samples that represent vegetation and run a supervised classification These steps will ...


4

Herein lies a misunderstanding: "...that is done separately for each band and not for a specific colour." "Each band" and "specific colour" are in fact the same thing. That is, it is the values in each band that, when combined together, make a specific colour. For example the RGB triplet 255,0,0 is the specific colour of pure red, comprised of band-red at ...


3

Try converting your color list from RGB format to HSV format and then sort the HSV list. What program did you get the RGB values out of? You might be able to tell it to simply report out HSV values. If you can't get HSV directly from that program, you could convert RGB to HSV here http://www.rapidtables.com/convert/color/rgb-to-hsv.htm Background: RGB ...


3

As a geologist, I make geological cross section using the elevations values from a DEM and the colors from a geological map, look at the pure Python solution with osgeo.gdal in Python Script for getting elevation difference between two points But now, since PyQGIS 2.x, it is easier with PyQGIS and the QgsRaster.IdentifyFormatValuefunction example with a ...


2

Seems the bugs you mention with GRASS are a known issue with the standalone version. Nothing to do with mapcalc... this was a packaging issue that has been solved on osgeo4w and now is just needed to wait for updated standalone installers. Réf : https://hub.qgis.org/issues/8529


2

As mentioned in comments, I am not aware of any out-of-the-box way for ArcMap to symbolize layers using RGB values stored in an attribute table, or using RGB values in Excel joined onto an attribute table. However, there is an existing ArcGIS Idea to Set symbol color from RGB values in attribute table so I recommend that you add your vote to that. Note ...


2

The feature you want is implemented as "Data defined properties". See Data-defined Styles in QGIS for a first announcement. It works for me this way: Load the data as delimited text, and save it as a shapefile with CRS EPSG:4326 WGS84 Right-click on the layer -> Properties -> Style Leave the topmost dropdown field at Single Symbol Click on Simple ...


2

The fourth value is alpha (i.e. RGBA), which you can ignore. The four value structure is expected. You can read the colour tables into native lists/dicts with GDAL. from osgeo import gdal gdal.UseExceptions() ds = gdal.Open(fname) band = ds.GetRasterBand(1) arr = band.ReadAsArray() ct = band.GetColorTable() # index value to RGB (ignore A) i2rgb = ...


2

rasterlite_load does have logic to handle imagery other than RGB, but it may not deal with your situation. From the code, the supported combinations are: bits_per_sample == 1 && samples_per_pixel == 1, interpreted as a CCITT 4 fax. bits_per_sample == 8 && samples_per_pixel == 1 && photometric == 3, interpreted as paletted image ...


2

You can use both options. For Option a) you don't need to create a specific SLD providing the image is RGB. GeoServer will pick the channels automatically, if you do indicate them separately you might cause unnecessary processing. Option b) requires less processing and should work fine but it might be slightly slower since you will do the transcoding from ...


1

gdal_translate is able to extract single bands: gdal_translate -b 1 in.tif out1.tif gdal_translate -b 2 in.tif out2.tif gdal_translate -b 3 in.tif out3.tif gdal_translate -b 4 in.tif out4.tif Raster -> Conversion -> Translate will create a gdal_translate command line, which you can edit to specify the band you want.


1

Yo uhave the municipality boundaries a a shapefile, and your jpeg has "homogeneous" values inside each municipality. Therefore, the best method to get your values is tansfer the values of eac band. It is easy if you have spatial analyst licence: 1) feature to point with the "inside" option to build the centroid (or add XY fields and make XY event layer if ...


1

I am guessing those mysterious spaces after G:\ are your attempt to make your file path anonymous? Your output file name is invalid. "cml.tif&_nred" is not a valid file geodatabase name. You cannot have symbols like "." or "&" in the raster name. A valid name would by something like "cml_tif_nred". Have a look at the model only Parse Path tool.


1

This does not probably work generally but it should give a correct result in your case. I captured your image above and saved it as "pngtest.png". I checked that it is a RGB png file with alpha channel. However, because it looks like a classified image I decided to try what happens if I convert it into a paletted tiff with GDAL tool rgb2pct.py ...


1

I would just add all of the rasters to a mosaic dataset (requires at least an ArcGIS Standard license), without any conversion. If you want to add info about the date, you can add a field to the attribute table of the mosaic's footprints (not even necessary if the name of the raster file is enough: it is added to the attribute table of the footprints when ...


1

ArcScan is meant to digitize drawings (typically scanned cadastral maps or the likes). In your case you want to classify an RGB image. With a true color image, you can use the image classification toolbar . You'll need to draw some sample polygons and use them for training a classifier. You need spatial analyst licence for that, and I would rather use an ...


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here's a better answer, use gdalbuildvrt with either srcnodata or vrtnodata flag: gdalbuildvrt -srcnodata "123 231 67" outfile.vrt input.tif If the next application in line doesn't understand .vrt, translate to a new tif: gdal_translate outfile.vrt final.tif


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To elaborate on @Stacky's comment: When opening the Layer Properties dialog box for a multiband raster and selecting the Symbology tab, the options available are "Stretched" and "RGB Composite". "Stretched" is useful when only a single band from the raster is needed, but "RGB Composite" is obviously appropriate for a raster with RGB bands. At the top of ...


1

If the output file format is not geotiff, rgb2pct.py creates an intermediate geotiff to write the results into before converting that to the final output format. The comments in the code state: # Create the working file. We have to use TIFF since there are few formats # that allow setting the color table after creation. From lines 127-129 of ...



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