Landsat clustering

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Land Use Clustering

Written by Tom Augspurger
Created: November 26, 2018
Last updated: June 30, 2020

Spectral Clustering Example

The image loaded here is a cropped portion of a LANDSAT image of Walker Lake.

In addition to dask-ml, we'll use rasterio to read the data and matplotlib to plot the figures. I'm just working on my laptop, so we could use either the threaded or distributed scheduler, but here I'll use the distributed scheduler for the diagnostics.

In [1]:
import rasterio
import numpy as np
import xarray as xr
import holoviews as hv
from holoviews import opts
from holoviews.operation.datashader import regrid
import cartopy.crs as ccrs
import dask.array as da
#from dask_ml.cluster import SpectralClustering
from dask.distributed import Client
hv.extension('bokeh')
---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
/tmp/ipykernel_6246/1760712241.py in <module>
----> 1 import rasterio
      2 import numpy as np
      3 import xarray as xr
      4 import holoviews as hv
      5 from holoviews import opts

ModuleNotFoundError: No module named 'rasterio'
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import dask_ml
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dask_ml.__version__
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from dask_ml.cluster import SpectralClustering
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client = Client(processes=False)
#client = Client(n_workers=8, threads_per_worker=1)
client
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import intake
cat = intake.open_catalog('./catalog.yml')
list(cat)
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landsat_5_img = cat.landsat_5.read_chunked()
landsat_5_img
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crs=ccrs.epsg(32611)
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x_center, y_center = crs.transform_point(-118.7081, 38.6942, ccrs.PlateCarree())
buffer = 1.7e4

xmin = x_center - buffer
xmax = x_center + buffer
ymin = y_center - buffer
ymax = y_center + buffer

ROI = landsat_5_img.sel(x=slice(xmin, xmax), y=slice(ymax, ymin))
ROI = ROI.where(ROI > ROI.nodatavals[0])
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bands = ROI.astype(float)
bands = (bands - bands.mean()) / bands.std()
bands
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opts.defaults(
    opts.Image(invert_yaxis=True, width=250, height=250, tools=['hover'], cmap='viridis'))
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hv.Layout([regrid(hv.Image(band, kdims=['x', 'y'])) for band in bands[:3]])
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flat_input = bands.stack(z=('y', 'x'))
flat_input
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flat_input.shape

We'll reshape the image to be how dask-ml / scikit-learn expect it: (n_samples, n_features) where n_features is 1 in this case. Then we'll persist that in memory. We still have a small dataset at this point. The large dataset, which dask helps us manage, is the intermediate n_samples x n_samples array that spectral clustering operates on. For our 2,500 x 2,500 pixel subset, that's ~50

In [ ]:
X = flat_input.values.astype('float').T
X.shape
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X = da.from_array(X, chunks=100_000)
X = client.persist(X)

And we'll fit the estimator.

In [ ]:
clf = SpectralClustering(n_clusters=4, random_state=0,
                         gamma=None,
                         kmeans_params={'init_max_iter': 5},
                         persist_embedding=True)
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%time clf.fit(X)
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labels = clf.assign_labels_.labels_.compute()
labels.shape
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labels = labels.reshape(bands[0].shape)
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hv.Layout([regrid(hv.Image(band, kdims=['x', 'y'])) for band in bands]) 
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hv.Layout([regrid(hv.Image(band, kdims=['x', 'y'])) for band in bands[3:]])
In [ ]:
hv.Image(labels)
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Download this project.