Clustering to find homogenous areas#

Short description

This notebook performs clustering analysis on Sentinel-2 satellite data, utilizing the B02, B03 and B04 bands to identify and group areas with similar spectral characteristics for further analysis.

In this notebook, you will search for, select, and obtain Sentinel-2 data for one day over a neighborhood in Barcelona, Spain. The selected data will be cloud-free to ensure accurate analysis of the study area. Specific bands, such as the B02, B03 and B04 bands will be calculated and obtained over the region of interest. A clustering analysis will be performed on these bands to group areas with similar spectral characteristics, enabling a deeper understanding of the landscape patterns. This example demonstrates the application of clustering techniques on Sentinel-2 data to identify and visualize distinct land cover types.

1 - Import spacesense object(s) and other dependencies#

[1]:

from spacesense import Client, geoutils
import datetime
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
from skimage import exposure

if "SS_API_KEY" not in os.environ:
    from getpass import getpass
    api_key = getpass('Enter your api key : ')
    os.environ["SS_API_KEY"] = api_key

Enter your api key : ··········

2 - Define AOI and output options#

[2]:

# A neighborhood of Barcelona
aoi = {
  "type": "FeatureCollection",
  "features": [
    {
      "type": "Feature",
      "properties": {},
      "geometry": {
        "coordinates": [
          [
            [
              2.1719121924506055,
              41.39760043017927
            ],
            [
              2.1647389059867805,
              41.39223018500084
            ],
            [
              2.1682818096665244,
              41.389200339725676
            ],
            [
              2.175746693142031,
              41.394800520638
            ],
            [
              2.1719121924506055,
              41.39760043017927
            ]
          ]
        ],
        "type": "Polygon"
      }
    }
  ]
}

[3]:

# Define the TOI
start_date = "2021-06-16"
end_date = "2021-06-16"

[4]:

client = Client(id="cluster_zones")

3 - Search S2#

[5]:

s2_search_result = client.s2_search(aoi=aoi, start_date=start_date, end_date=end_date, query_filters={"valid_pixel_percentage": {">=": 99}})
s2_search_result.dataframe

WARNING:spacesense.core:start_date and end_date are the same, adding 1 day to end_date

[5]:

	id	date	tile	valid_pixel_percentage	platform	relative_orbit_number	product_id	datetime	swath_coverage_percentage	no_data	cloud_shadows	vegetation	not_vegetated	water	cloud_medium_probability	cloud_high_probability	thin_cirrus	snow
0	S2B_31TDF_20210616_0_L2A	2021-06-16	31TDF	99.91	sentinel-2b	008	S2B_MSIL2A_20210616T103629_N0300_R008_T31TDF_2...	2021-06-16T10:49:42Z	100.0	0.0	0.0	1.83	98.08	0.0	0.0	0.09	0.0	0.0

[ ]:

#We remove duplicate dates
s2_search_result.filter_duplicate_dates()

4 - Specify bands#

Only selecting bands from S2 that we are interested in. In this urban example, we choose the RGB bands (2,3 and 4)

[6]:

s2_search_result.output_bands = ["B02","B03","B04"]

6 - Look at the RGB image#

[8]:

fuse_result.plot_rgb(all_dates = True, brightness_factor = 2)

WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

../_images/notebooks_10_clustering_with_S2_15_1.png

7 - Cluster S2 data with the cluster function#

In this example, we only use a single image. However, the Cluster function can also accept multi-temporal datasets to perform its K-mean clustering.

[9]:

# Select the number of clustering classes you want
n_clusters = 3
# What variables are to be taken into account in the clustering?
variables_to_use = [ "S2_B02", "S2_B03", "S2_B04"]
# In this example we take all the dates from fuse_result.dataset, but you can select a single date as well)
clustered_dataset_RGB = geoutils.cluster(fuse_result.dataset, n_clusters, variables_to_use)

/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(

8 - Plot the clustered function using the util function#

[10]:

geoutils.plot_clustered_dataset(clustered_dataset_RGB, n_clusters)

../_images/notebooks_10_clustering_with_S2_19_0.png

Clustering to find homogenous areas

Contents