Lab 4: Miscellaneous Image Functions

Goal and Background

The main purpose of this lab is to:

• Define a study area from a larger satellite image
• Learn how spatial resolution of images can be enhanced for visual interpretation purposes
• Work with new radiometric enhancement techniques
• Linking satellite images with Google Earth 
• Learn methods of resampling satellite images
• Practicing methods of image mosaicking
• Learn binary change detection with the use of simple graphical modeling

Working with and learning these new skills are vital to the study of remote sensing.  These skills are practiced by using the ERDAS Imagine software.

Methods

Part 1: Image subsetting

• The first part of this lab worked with image subsetting and creating an area of interest (AOI) for a study area.  The AOI was created through using an inquire box.  The box was placed around the Eau Claire area and the subset was created.  This subset is pictured in Figure 1 below.

Figure 1: Eau Claire subset

• Next we created another subset, but we used a shape file in order to focus on a more specific AOI.  This was done by adding the shape file to the original raster that was used for the first subset.  The focused subset that was created is pictured below in Figure 2.

Figure 2: Eau Claire subset created with a shape file

Part 2: Image fusion

• In this section of the lab I created a higher spatial resolution image from a coarse resolution image.  This is done in order to enhance the image spatial resolution for visual interpretation functions.  First, I used the resolution merge tool in order to enhance the resolution from 30 meters in a reflective image to 15 meters from a panchromatic image.  This process resulted in a pan sharpened image with a resolution of 15 meters.  These three images are pictured in Figures 3.1, 3.2, and 3.3 below.

3.1: Panchromatic image, 15 meter resolution.

3.2: Reflective image, 30 meter resolution.

3.3: Pansharpened image, 15 meter resolution.

Part 3: Simple radiometric enhancement techniques

• This section focused on haze reduction in images.  I used the haze reduction tool and it enhanced the color of the image and overall clarity of it.  This tool is useful, but the resolution is not enhanced with the haze reduction. The original and haze reduced image are picture below in Figures 4.1 and 4.2.

Figure 4.1: Original image with haze.

Figure 4.2: Haze reduced image.

      

                                                                                              

Part 4: Linking image viewer to Google Earth

• I thought this part of the lab was very interesting.  It really showed the difference a high resolution satellite can make.  I opened an image of the Eau Claire area in ERDAS Imagine and opened another window that connected to Google Earth.  I synchronized the views and zoomed into where my house is in Eau Claire.  The image in ERDAS was completely pixelated and I couldn't make out any objects or significant features.  In the Google Earth window I could see my house clearly with no pixelation.  Google Earth uses GeoEye high resolution satellites and it's incredible the difference those satellites can make.  Zoomed out to their full extent these images look the same, but once you zoom in the differences are immediate.

Part 5: Resampling

• In this section I resampled an image with two different methods, nearest neighbor and bilinear interpolation.  The nearest neighbor and bilinear interpolation methods did not create a very noticeable difference compared to the original image.  The two processes had the pixel size changed from 30x30 to 15x15, which I thought would have made a big difference, but in the end the difference was not very noticeable unless you know what you're looking for.  I also debated about putting the three images in this blog post, but the differences are so indistinguishable at a large size that I decided not to include them.

Part 6: Image Mosaicking

• Two different types of mosaicking were used in this section, Mosaic Express and Mosaic Pro.  Mosaicking is basically taking two or more images and stitching them together in order to create on big image.  Mosaic Express is a quick and easy way to create one full image, but the final product does not look professional.  In order to get a better image I used Mosaic Pro.  This takes more effort and decisions to create, but ultimately the picture looks better overall and gives the user a better look at the two images as one.  The two mosaics are pictured below in Figures 5.1 and 5.2.

Figure 5.1: Mosaic created with Mosaic Express

Figure 5.2: Mosaic created with Mosaic Pro

Part 7: Binary change detection (image differencing)

• In the last section of the lab we used the model building tool.  The tool was quite simple and helped create the map pictured in Figure 6 below.  I inputted a map from August of 1991 and entered an equation to create an output to show pixel differentiation from August of 1991 to August of 2011.

Figure 6: Pixel changes from 1991 to 2011

Results

Pictured below are the images I created throughout this lab using different tools.

Figure 1: Eau Claire subset

Figure 2: Eau Claire subset created with a shape file

3.3: Pansharpened image, 15 meter resolution.

Figure 4.2: Haze reduced image.

Figure 5.1: Mosaic created with Mosaic Express

Figure 5.2: Mosaic created with Mosaic Pro

Figure 6: Pixel changes from 1991 to 2011

Conclusions

This lab really helped me learn more of the basic techniques used in ERDAS Imagine and in the study of remote sensing.  Knowing these simple tools allows me to critically look at remotely sensed images while also altering them in order to get the information I need.