Lab 5: Image Mosaic and Miscellaneous Image Functions 2

Goal

This lab is designed to introduce you to some important analytical processes in remote sensing. The lab explores RGB to IHS transform and back, image mosaic, spatial and spectral image enhancement, band ratio, and binary change detection. The image mosaic adopted in this lab is structured to teach you how to process individual scenes of satellite images to arrive at one seamless scene, in the event you are faced with a project that covers a large geographic area that exceeds the spatial extent of one satellite image scene, or your study area astride portions of two satellite image scenes. At the end of the lab exercise, students will be in a position to apply all the analytical processes introduced in this lab in real life projects.

Methods

Part 1: RGB to IHS and IHS to RGB

In the first part of the lab we had to transform images from Red, Green, Blue (RGB) to Intensity, Hue, Saturation (IHS) and then back to a RGB image.  Various steps were followed in ERDAS in order to come up with the specific results.  The RGB to IHS function transform an image that is suppose to produce a more realistic image.  The IHS image is an image that has different band combination.
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Part 2: Image Mosaicking

In the next part of the lab we were asked to mosaic images together using two different ways.  The two ways are mosaic express and mosaic pro.  The first method we used was mosaic express.  Mosaic Express is the quicker way to mosaic an image and does not always produce the same results.  The second method we used was Mosaic Pro.  Mosaic Pro has you set parameters in order to produce the results we intended.  

Part 3: Band Ratioing

Band ratioing is a very help tool in order to pick out and exemplify a specific band within the image.  In this part of the lab, we used band ratioing on the normalized difference vegetation index (NDVI) image.  In the image you can tell exactly where vegetation is growing and where it is not growing.  This tool is very helpful when using an image with vegetation in or you are trying to find vegetation.
  

Part 4: Spatial and Spectral Image Enhancement

Spatial and spectral enhancement are a techniques applied to images to improve on its appearance for visual analysis.  These change the contrast and brightness in order for the human eye to be able to perceive it.  For example, we were given a high frequency image and we had to tone down the brightness level of the image.  We were also given a low frequency image and we had to do a high and low pass convolution mask/edge enhancement to improve the brightness.  In the last part of this section we had to equalize the contrast on a histogram to improve the image quality.  These are all useful tools in order to be able to better perceive the image.

 Part 5: Binary Change Detection (Image Differencing)

Binary change detection is a useful tool in order to see the difference between years on images.  For example, in our lab we used images from 1991 and 2011 to look at the change in a four county area in Western Wisconsin.

Results

The following images are a select few from the important tools that we learned how to use in ERDAS imagine in this lab.  The first image is a transformation from IHS to RGB.

Figure 1: The image above is a transformation IHS to RGB. The color change is from false color to real color.

Figure 2: The image above is of a stretched RGB.

Mosaic Express

Figure 3: The image above is of a mosaic express.  As you can see there is not a smooth transition between images.

Mosaic Pro

Figure 4: The image above is of a mosaic pro.  As you can see there is a smoother transition between images.

Conclusion

This lab was extremely beneficial to learn new tools in the ERDAS Imagine program.  In this lab we learned tools to transform images from RGB to IHS and IHS back to RGB, image mosaicking, band ratioing, spatial and spectral image enhancement, and binary change detection.  Overall, all these tools are very necessary to learn in order to be able to read remotely sensed images.