Remote Sensing Lab 5

This lab was all about analytical processes in remote sensing and learning how and when to use each method. The processes that are included in this lab are image mosaic, spatial and spectral image enhancement, band ratio, and binary change detection. The goal is that by the end of the lab I am able to use these skills and also know when to apply them to a project in the future that I may be working on.

The first part of the lab was dealing with image mosaicking. Image mosaicking is used when the area that you are interested in spans across more than one satellite image. You then use the mosaic to seamlessly combine the two images into what appears to be one image which includes your whole study area.
There are two different kinds of image mosaic in ERDAS Image 2013. The first is called mosaic express which is the easier and less user intensive way. MosaicPro is much more user intensive and you have a lot more control of the options that are selected for the mosaic. You also get a better mosaic from Pro than you do with express. As you can see the colors of the two images in the Mosaic Express image do not match up very well, this is one of the drawbacks of mosaic express. The MosiacPro image is a very good mosaic you can hardly tell where one image starts and where the other ends, this is exactly what you want. The colors match up very well and it is a seamless transition from one image to the other.

Mosaic Express Image

MosaicPro Image

The next part of the lab was all about band ratioing. I did a band ratio using the normalized difference vegetation index (NDVI). Pretty much what I did was take an image that shows vegetation density by different shades of red and turned it into an image that displays the vegetation values in grayscale so that it is easier to tell the difference between shades. In the new image very white places represent heavy vegetation and the darker gray and black areas represent places with little or no vegetation. The more vegetation the brighter the area is on the output.

After NDVI

Before NDVI

Part 3 was all about spatial and spectral image enhancement techniques. First I did a spatial enhancement. The image I was given is an example of a high frequency image. High frequency images have a very large brightness difference over a short distance which can make the image harder to interpret. In order to fix this I applied something called a low pass convolution filter. This filter lessens the brightness differences in an image making it easier to interpret.

Before Low Pass Filter

After Low Pass Filter

You can also have low frequency images, or images that have very little brightness difference over a short distance. These images are often too similar in brightness values throughout which makes picking out features difficult. For an image like this I applied a high pass convolution filter. This kind of filter makes the differences between light and dark more drastic in the image to help with interpretation.

Before High Pass Filter

After High Pass Filter

 Another kind of spatial enhancement is called edge enhancement. I did this to make features in the image stick out more and be more visible. I did this by using a laplacian convolution filter. A laplacian filter sharpens an image by increasing contrast in certain spots called discontinuities. In the image after edge enhancement was applied you can see that the rivers stand out as very bright green.

Before Edge Enhancement

After Edge Enhancement

 Next I used a spectral enhancement tool called linear contrast stretch to improve the visual appearance of images. The first linear stretch is a minimum-maximum contrast stretch. In order to perform this kind of stretch the image has to be Gaussian. This means that when you look at the histogram of the image there is only one mode or "hill". When you apply this stretch it takes the two ends of the mode and pulls them in opposite directions stretching the histogram all the way across the 0-255 range increasing the contrast of the image. Most images are non-Gaussian which limits the use of this stretch method.

Before Min-Max Stretch

After Min-Max Stretch

The second linear stretch method is piecewise stretch. This stretch is more common and is used on non-Gaussian images or ones that have multiple modes or "pieces" in the histogram. When you do this stretch you have to select the beginning and end point of each mode and then the histogram is again stretched across the full 0-255 range increasing contrast.

Before Piecewise Stretch

After Piecewise Stretch

The final part of the lab was on binary change detection or image differencing. Image differencing is exactly what it sounds like you are taking two images and looking at the differences in them. For this lab the differences I was looking at were the brightness changes of pixels in two images taken 10 years apart. To do this I opened the two images and have ERDAS Image 2013 find the pixels that have lost brightness from one image to the other by creating a model. A model is a mathematical equation but you build it in the form of pictures and symbols like creating an electrical circuit model. I put the two images in the modeler and then chose a function for it to calculate. In this case that function is loss of brightness in a pixel. When the program runs this function it creates an output or image of all of the pixels that lost brightness. I ran this model again only this time the function was to find all the pixels that did not change in brightness. It runs that function and outputs an image of those pixels. You then take those two images that it output and put them on top of each other in ArcMap. This allows you to create and map of the pixels that changed and display them in one color and the ones that did not change in another color. The map below is the map I created using this method, the green pixels changed or lost brightness the other pixels did not change.  

Map Created From Image Differencing

Sources:

ArcMap

ERDAS Image 2013