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4

I hacked together a solution for this and wrote a blog article a while back on a very similar topic, which I will summarize here. The script is intended to extract a river from a 4-band NAIP image using an image segmentation and classification approach. Convert image to a numpy array Perform a quick shift segmentation (Image 2) Convert segments to raster ...


3

You could look at clustering in scikit-learn. You will need to read the data into numpy arrays (I'd suggest rasterio) and from there you can manipulate the data so that each band is a variable for classification. For example, assuming you have the three bands read into python as red, green, and blue numpy arrays: import numpy as np import sklearn.cluster ...


2

Make sure you're using a 64 bit build of QGIS. The limitation on exported composer sizes/DPI is much higher on a 64 bit build.


2

When clipping the image it is likely that you removed the edge of the image. The edge of Landsat TM imagery is assigned 0 in all bands. This will result in 0 no longer being the minimum and a significant increase in the mean value across the raster. Furthermore, I would assume that you have also clipped the image to no longer include clouds, which would ...


2

1. where should I begin? Do you know what Image Classification is? If not here's an intro article ESRI wrote about for arcgis. You don' need arcgis to read it. Read it, and in the end you'll understand what you should need. Keep in mind that image classification is about creating classes. To do that should well defined classes beforehand (how many, ...


1

Your last attempt looks very promising. With more than 5 points you might get an even better picture. I use this transformation settings: Using as many border points as possible for georeferencing, I take the coordinates from the map canvas with the middle icon: and get this picture (with clipping to GADM borders):



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