Classification using ENVI 5.2

Prof. P. Lewis & Dr. M. Disney

Remote Sensing Unit

Dept. Geography

UCL

Aims

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.

Advanced use of these notes

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):

1. 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

2. Copy all of the notes to your local computer (if for an update)

    cd ~/DATA/working/geog2021
   
    git reset --hard HEAD
   
    git pull

3. Run the notebook

    ipython notebook ClassificationIntro.ipynb

Contents

[top] [Introduction] [Examination of the data] [Defining spectral classes] [Image Classification] [Accuracy Assessment] [Further Work] [Summary]

Data

The datasets you need for this practical are available from:

• ETM-110801
• ETM-110801.HDR
• TM-250792
• TM-250792.HDR
• SRTM-2002
• SRTM-2002.HDR

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.

1. Introduction

In this section, we load the image data we wish to explore.

Importantly, we have the ability to map these changes from the archive of satellite data, particularly data from the Landsat series of satellites. An excellent introduction to visualising environmental change from Landsat data is given by Jeffrey Kluger.

Using data such as these, we can 'track' the changes in land cover over time.

For example, below we show data produced by Google and Dr. Matthew Hansen at the University of Maryland which shows global maps of forest change (2000-2012) using Landsat data (see Science article), with red showing loss in 2013 through to yellow for the year 2000 (using pseudocolour).

The purpose of this practical is for you to perform and test a land cover classification over this area, using data from two dates (1992 and 2001). The visualisations above show that there has been significant change since 2001 (and before 1992).

We will be doing this using separate classifications of the two image dates, but you should be thinking throughout about whether this is an appropriate method, and what else you might consider (especially if you had a long time series of data such as those shown in the animations).

We will be doing a supervised classification here.

The steps you will undertake are:

• Examine the data and explore the spectral characteristics
• Define a series of Regions of Interest (ROIs) describing the classes you wish to extract
• Perform the classification
• Test the result

First, obtain and then load the TM and ETM images of Rondonia noted above, along with the SRTM DEM file.

View the ETM image as a FCC.

You may need to edit the image file to associate the DEM data correctly. To do this, look under 'Raster Management' in the Toolbox, and edit the ENVI header (for the Landsat data). You should then edit the header attributes to associate the DEM with the image data.

[top] [Introduction] [Examination of the data] [Defining spectral classes] [Image Classification] [Accuracy Assessment] [Further Work] [Summary]