Prof. P. Lewis & Dr. M. Disney
Remote Sensing Unit
Dept. Geography
UCL
After completing this practical, you should be able to analyse one or more image datasets using classification methods. This includes learning to identify land cover classes in a dataset and consider class separability (using histograms, scatterplots and other tools), and applying and assessing a classification product using Envi.
Although it is perfectly adequate to simply view the html (webpage) of these notes, there are some additional features in these notes that you can use (in this case, a convolution tool with sliders) if you access them in a different way. The reason this is possible is that these notes are written in an ipython notebook.
To use the notes as a notebook (assuming you have git
and python on your computer):
Copy all of the notes to your local computer (if for the first time)
mkdir -p ~/DATA/working
cd ~/DATA/working
git clone https://github.com/profLewis/geog2021.git
cd geog2021
Copy all of the notes to your local computer (if for an update)
cd ~/DATA/working/geog2021
git reset --hard HEAD
git pull
Run the notebook
ipython notebook ClassificationIntro.ipynb
The datasets you need for this practical are available from:
You should download these data and put them in a directory (folder) that you will remember!
The data you will be using are:
six wavebands of a Landsat TM image over Rondonia, Brazil, imaged on 25th July 1992. The data are at an original pixel spacing of 28.5 m.
six wavebands (nominally the same wavelengths) of a Landsat ETM image with the same spatial resolution, covering the same spatial extent. These data were obtained on 11th August 2001.
Digital Elevation model (DEM) data, obtained by RADAR interferometry from data on the SRTM (Shuttle Radar Topography Mission), are also available for the site. The data have been resampled to the same reolution and area as the TM/ETM data above.
The wavebands are:
1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|
450-520 nm | 520-600 nm | 630-690 nm | 760-900 nm | 1550-1750 nm | 2080-2350 nm |
The extent of the imagery is (Lat/Lon):
$$ 11^o 1' 31.29'' S, 62^o 58' 27.57'' W \rightarrow 11^o 57' 4.75'' S, 62^o 1' 55.96'' W $$The full SRTM data can be loaded into google earth, if you have access to this.
Although you have the data 'pre-packaged' for this practical, you can download your own datasets using the USGS Glovis tool:
We can of course explore the area in Google Maps, which we may find useful for exploring the classification.
In [28]:
# Don't worry about this -- its just to display the google maps
from IPython.display import HTML
HTML('<iframe src=gmRondonia.html width=100% height=350></iframe>')
Out[28]:
In this section, we load the image data we wish to explore.
In [19]:
run python/video.py
In [20]:
video('images/rondonia_deforestation_medium.mp4', 'x-m4v')
Out[20]: