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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 ...


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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 ...


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You can use the OpenCV package in Python for image thresholding. This example shows not only how to perform the binary image thresholding, but also the limitations of this method. Here, I use a 1m spatial resolution NAIP image that shows a dirt road surrounded by arid vegetation. You can see that the road is extracted but there is also a significant amount ...


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I have also had a lot of success using the image classification tools in ArcGIS (http://resources.arcgis.com/en/help/main/10.2/index.html#//00nv00000008000000). The documentation is great and the results have been very accurate. Unsupervised classification is tricky because defining the number of classes will always result in some degree of mixing. Even ...


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If you use QGIS, there is a plugin named "semi automatic classification". It may not take too much time to utilize the plugin because you might be familiar with the RS analysis methods. I have used it for 1 week and have been pleased. The plugin is also capable of downloading landsat's photos. Here's the link of classification tutorial. ...



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