Lab 4: Miscellaneous Image Functions 1

Lab 4: Miscellaneous Image Functions

 

Goal

     The goal of Lab 4 is to become familiar with image functions in Erdas Imagine 2013. The functions we will be looking at include creating a subset of an image, image fusion techniques, radiometric enhancement techniques, and resampling pixel size, which are basic functions that can be used in any remote sensing project. We will be employing these techniques on false color satellite images taken by Landsat satellites over the Eau Claire, WI area (Figure 1).

 

Methods

     The first image function we looked at is creating an image subset which decrease file size and speeds up image processing. Subsets can be created in two ways. The first technique creates a subset using an inquire box. To do this you create a rectangular box around the study area and extract the pixels that are found within the box (Figure 2). The second method creates a subset by extracting the pixels that are located within an Area of Interest shapefile. This second method is useful for creating subsets with oddly shaped boundaries, such as county boundaries or lakes.

     The next image function we looked at is pan-sharpening. This techniques combines coarse resolution reflective images and combines them with high resolution panchromatic images to create a high resolution color image (Figure 3). To pansharpen an image we selected the "Resolution Merge" option under the "Pan Sharpen" tool. We then input the high resolution panchromatic image, the multispectral image, and the output file location. Next we select the pansharpening method, in this case multiplicative. We also select the resampling method, nearest neighbor. Finally, we run the model and add the pansharpened image into the view to compare it to the original multispectral image (Figure 4).

     The third technique we looked at is haze reduction. To apply a haze filter we select the "Haze Reduction" option under the Radiometric tab. Select the input image and the output location. In this case we accepted all the default settings and ran the program.


     Finally, we looked at resampling an image. This technique allows you to change the pixel size of an image to match the pixel size of another image. In this lab we resampled an image of Eau Claire with 30 meter spatial resolution down to 20 meters using the nearest neighbor and bilinear interpolation methods.

 

Results

     The process of creating an image subset is a really important process to decrease file size and increase computation speed. The 2 most common ways to create subsets include the use of an  inquire box (Figure 2) and the use of a shapefile of the study area (Figure 3). Inquire box's are good for creating rectangular subsets. However, in many cases the study area is is irregularly shaped. In these cases it is appropriate to use a shapefile to extract the subset. Figure 3 is an image subset that was created using a shapefile of Eau Claire and Chippewa Counties in West Central Wisconsin.




Figure 2  Image subset of Eau Claire, WI using an inquire box.

  

Figure 3  Image subset of Eau Claire and Chippewa County using a shapefile.

     The next technique we examined is pan sharpening. Pan sharpening increases the spatial resolution of reflective bands of an image by combining it with the panchromatic band of an image. The results of a pansharpened image (Figure 4) are incrased spatial resolution and an increase in contrast.

Figure 4 The effect of pan sharpening an image (left) compared to the original image (right).

     The next technique we examined is the process of haze reduction (Figure 5). This technique reduces haze in an image. This can be useful in areas where significant haze is present. However, in areas where no haze is present the image becomes less clear. Because of this problem we only apply haze to sections of the image that contain a significant amount of haze. 

Figure 5 Haze reduction of an image (left) compared to the original image (right).

 

     Finally, the last image function we examined is resampling. Resampling is a mathematical technique used to change pixel size. Resampling is frequently used when comparing 2 images with different pixel size. In this lab we looked at 2 common methods for resmapling (Figure 6). The Nearest Neighbor technique (left) uses the brightness values of the closest input pixel to assign values of the output pixel. The nearest neighbor technique is useful for the resampling of thematic rasters. As you can see below, the output image has a rough appearance and curved features often have a stepped appearance. Another technique of resampling that we looked at is Bilinear Interpolation (right). Bilinear interpolation creates an output image by averaging the 4 closest input pixels. Bilnear interpolation creates a much smoother image that is more spatially accurate. However, bilinear interpolation also reduces image contrast.

Figure 6  Common resampling techniques of Nearest Neighbor (left) and Bilinear Interpolation (right).

  

Conclusion

     In conclusion, there are many image function techniques that can be used to enhance satellite images. Creating a subset can reduce file size and increase processing speed,  pansharpening increases spatial resolution of reflective images, haze filters reduce the amount of haze in an image that creates distortion, and resampling changes pixel size so images from different sources can be compared.