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The purpose of this exercise is to learn preliminary image processing and information extraction from multi-spectral images. To do this assignment, a Landsat image set over Melbourne and the ENVI software will be used. The assignment involves working with images captured at different wavelength bands and combining images to analyse vegetation.


Radiometric enhancement is often a preliminary step in image interpretation and information extraction from aerial and satellite images. An example of radiometric enhancement techniques is histogram stretching. This technique is used to increase the image contrast and improve the visual quality of the image. Another useful technique is creating band ratios for combining multispectral images to highlight various features. Briefly, this technique involves an arithmetic operation on multiple bands resulting in a new image. Band ratios are used to highlight spectral signatures of different objects. For example, healthy vegetation has low reflectance in the red wavelengths of the electromagnetic spectrum and high reflectance in the near-infrared wavelengths. Therefore,by dividing a near infrared band by a red band we can create a new image in which healthy vegetation is highlighted by large values whereas everything else has low values. Vegetation indices like Normalized Difference Vegetation Index (NDVI) can further help distinguish healthy vegetation from stressed vegetation.


A Landsat-8 dataset of Melbourne acquired on 31 August 2019 will be used for this assignment.Information about the resolution and wavelength bands of Landsat-8 images can be found on the Landsat-8 website:

ENVI will be used for reading the dataset and processing the images. Information about the different processes can be obtained from the software Help documentation (Menu bar > Help >Contents).


The assignment consists of three main tasks:
1. Applying radiometric enhancement
2. Combining the images to create band ratio images including NDVI
3. Performing image classification.You should be able to complete each task in one lab session. The whole assignment should be completed within three weeks.
Task 1: Applying radiometric enhancement techniques

In this task, you will be able to visualize different bands of the image and use the histogram to adjust the illumination, contrast and brightness.
1. Unzip the dataset file into a folder in your local disc.
2. Start ENVI and open the dataset.
3. Right click on the image layer in the Layer Manager panel (left side of the software) and choose Zoom to Layer Extent. Now, you can see the whole image.
4. Right click on the image layer in the Layer Manager panel (left side of the software) and choose Change RGB Bands … to select different bands for different colour channels. Try different band composition such as true colour (Default), the false colour (B3 to Blue, B4 to Green and B5 to Red), and a full IR false colour image (B5 to Blue, B6 to Green and B7 to Red).
5. Stretch the image histogram for each band using the Histogram Stretch button which is in the main tab. By dragging vertical lines in Histogram Stretch window, histogram for each band is manipulated and you can see the change on the image. Also, you can choose various stretching method from the drop-down Stretch Type button. A good stretching can be done by Linear type.
6. Explore the image using the zoom and pan tool in image display.
7. By clicking on Data Manager icon , you can select various bands from the dataset. For instance, select a Thermal Infrared band and press Load data. Remember to stretch the image once you have displayed it.
8. The image is now displayed as a grey tone image (grey colour). You can change the colour by right clicking on the band and choosing Change Colour Table.Now we will create simple ratio images and combine them to make useful images like NDVI.

Task 2: Creation of ratio images and combining them together
We will create the following ratio images:
• B2/B5 to highlight water.
• B5/B4 to highlight vegetation.
• B7/B2 to highlight soil/clay.

1. Start ENVI and open the Landsat-8 dataset.
2. On the Toolbox pane, find Band Algebra Key and double click on Band Ratios.
3. In the prompted window, select the bands of a particular ratio for the numerator as well as the denominator. Next, press Enter and then OK.
4. In the following window, enter output file name as well as the saving directory. If you are creating the band ratio image highlighting water, name the image Water, for instance.
5. Having done that, the grey scale image will be shown on the display window.
6. Repeat the process for Vegetation and Soil band ratio. Remember to take a snapshot of each band ratio image as you need them for your report.Now you have three ratio images each highlighting a certain feature. The next step is to combine these in a colour composite visualization.
7. Click on Data Manager Key, from drop-down Band Selection button, select the red, green and blue layers as band ratio image for soil, vegetation and water, respectively.
8. By pressing Load Data, you will see the band composite on the display window.
9. Save this image and take a snapshot of it.Now, you will create an NDVI image.
11. From the Toolbox pane, navigate Spectral > Vegetation, then double click on NVDI.
12. Select the multispectral image set then press OK.
13. In NVDI Calculation Parameters window, select band 4 as the Red band and band 5 as the NIR band.
14. Choose the storing directory and name the image NDVI and then press OK.
15. You will see the NDVI image on the display window in grey scale. You can try various colour table from Change Colour Table.
16. Take a screenshot of your image and save your image. Don’t forget to add a Colour Bar
(Toolbar > Annotations > Colour Bar).

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