This is the code repository for summer research done through the NASA Space Grant Summer 2019 Undergraduate Research Program (SURP).

At the moment, the file path is hard coded into the seg_coast.py script and will need to be edited with the correct file path. The repository contains a "pickled" version of the segmented image which can be accessed by first running the command

tar -zxvf seg_pic.tar.gz

in the terminal, then in your python code adding the following:

import pickle

with open('seg_pic', 'rb') as f:
     img = pickle.load(f)

Running the K-means clustering algorithm takes approximately five minutes, so a "pickled" version of a clustered image is saved in the example files as k-means-pickle.tar.gz which can be used for analysis.

Run make in the terminal before running the border removal algorithm in order to compile the required cython code to C. Just for a sanity check, there should be a .so file now in the directory.

Images are assumed to have little cloud coverage and small amounts of sea ice to get contrast between water pixels and glacier pixels. Future implementations will have quality checks and assert cloud coverage be below a certain threshold.