![Picture](/uploads/9/8/8/8/98883300/rgb-mapper_orig.png)
In the original image provided “A2Data” there are three bands that express noise and artifacts. When first viewing the RGB Mapper for the image, you see that the bands are out of order. You can either put the bands back in order, or you can be vigilant when selecting the bands you want to analyze. I chose to keep the bands as is, which caused some difficulty as I forgot several times that the band sequence is not in order. Due to this, I used the wrong bands for the analysis, but was vigilant enough to catch my errors prior to moving forward with the final product. In the original image, bands TM1, TM2, and TM3 are not clean. Below you can find a description as well as images for issues found in each of the bands, and the solutions used to solve them.
The two main filters used to reduce or remove the issues include: MNFRN (Maximum Noise Fraction Noise Removal) and LRP (Image Line Replacement).
MNFNR: Attempts to remove noise in a single image band using a procedure based on the Maximum Noise Fraction transformation. The downfall to using MNFNR is that we lose depth in the red band.
LRP: Replaces any line of image data that is presumed damaged in the input image file. A line can be replaced with the line above it, the line below it, or the mean of the lines above and below it.
The two main filters used to reduce or remove the issues include: MNFRN (Maximum Noise Fraction Noise Removal) and LRP (Image Line Replacement).
MNFNR: Attempts to remove noise in a single image band using a procedure based on the Maximum Noise Fraction transformation. The downfall to using MNFNR is that we lose depth in the red band.
LRP: Replaces any line of image data that is presumed damaged in the input image file. A line can be replaced with the line above it, the line below it, or the mean of the lines above and below it.
TM1 – Blue Band
Issue: Noise – there were many noisy pixels found in the band, making or an image that is unclear. There is also a hazy line that cuts across the bottom left corner that affects the quality of the image. In the uncleaned image, we can see in the parts circled in red that there is some noise in the image resulting in an image that is not sharp, and hard to view detail. The effects in this band could be a direct result of rayleigh scattering. "Rayleigh scattering is most sensitive to shorter wavelengths such as the blue light with wavelengths between 0.4 and 0.5 µm" (Mather & Koch, 2011, 16).
Solution: MNFNR was used to help reduce any noisy pixels, as well as to remove the hazy line that cuts across the bottom of the map.
Solution Steps: Using the algorithm librarian, search for MNFNR. Once opened, select TM4 and TM5 as your output parameters. We chose both of these bands because they were the only two bands in the image that were completely clean. For the input parameter, you need to chose the band that you would like to clean. For this first filter, the input parameter was TM1. Once you have selected the appropriate bands, you can run the filter and the outcome will be a cleaned image.
Issue: Noise – there were many noisy pixels found in the band, making or an image that is unclear. There is also a hazy line that cuts across the bottom left corner that affects the quality of the image. In the uncleaned image, we can see in the parts circled in red that there is some noise in the image resulting in an image that is not sharp, and hard to view detail. The effects in this band could be a direct result of rayleigh scattering. "Rayleigh scattering is most sensitive to shorter wavelengths such as the blue light with wavelengths between 0.4 and 0.5 µm" (Mather & Koch, 2011, 16).
Solution: MNFNR was used to help reduce any noisy pixels, as well as to remove the hazy line that cuts across the bottom of the map.
Solution Steps: Using the algorithm librarian, search for MNFNR. Once opened, select TM4 and TM5 as your output parameters. We chose both of these bands because they were the only two bands in the image that were completely clean. For the input parameter, you need to chose the band that you would like to clean. For this first filter, the input parameter was TM1. Once you have selected the appropriate bands, you can run the filter and the outcome will be a cleaned image.
TM2 – Green Band
Issue: Scanner Error – There is a section of the image on the top of the map where the scanner missed a line of 820 pixels. There is also a hazy line that cuts across the bottom left corner that affects the quality of the image. This structured noise is most likely caused by "interference amongst electronic components" (Ryan, 2003), or in other words it is most likely also a scanner error. In the uncleaned image, we can see in the parts circled in red that there is some noise in the image, and the scanner error is highlighted.
Solution: LRP was used first to fix the scanner error at the top of the map. MNFNR was then used after on top of the LRP results to further clean the image and remove the hazy line cutting across the bottom and any unnecessary noise in the rest of the image.
Solution Steps: Before using the algorithm librarian, you need to click on the +xy button or cursor control tool in the tool bar. To solve for line error, you need to collect the parameters of the line you want to remove. For TM2, the parameters are 10, 38 in top left corner of line, 830 pixels across, and 1 down. Once you have these, you can go to the algorithm librarian and search for LRP. Open LRP and in the parameter tab, you'll enter: 0,38,830,1. Once the parameters are entered you'll go back to the file tab and select TM2 as the input file to fix. Once selected, you can run the filter and the LRP tool will be applied.
This band however, needed more than just the LRP filter. Noise is also evident throughout the image as well. After applying the LRP filter, an MNFNR filter is needed. Once the MNFNR filter is opened, select TM4 and TM5 as your output parameters. We chose both of these bands because they were the only two bands in the image that were completely clean. For the input parameter, select TM2. Once the appropriate files are selected, you can run the filter.
The final image will have both the LRP and MNFNR filter applied, leaving you with a cleaned image.
Issue: Scanner Error – There is a section of the image on the top of the map where the scanner missed a line of 820 pixels. There is also a hazy line that cuts across the bottom left corner that affects the quality of the image. This structured noise is most likely caused by "interference amongst electronic components" (Ryan, 2003), or in other words it is most likely also a scanner error. In the uncleaned image, we can see in the parts circled in red that there is some noise in the image, and the scanner error is highlighted.
Solution: LRP was used first to fix the scanner error at the top of the map. MNFNR was then used after on top of the LRP results to further clean the image and remove the hazy line cutting across the bottom and any unnecessary noise in the rest of the image.
Solution Steps: Before using the algorithm librarian, you need to click on the +xy button or cursor control tool in the tool bar. To solve for line error, you need to collect the parameters of the line you want to remove. For TM2, the parameters are 10, 38 in top left corner of line, 830 pixels across, and 1 down. Once you have these, you can go to the algorithm librarian and search for LRP. Open LRP and in the parameter tab, you'll enter: 0,38,830,1. Once the parameters are entered you'll go back to the file tab and select TM2 as the input file to fix. Once selected, you can run the filter and the LRP tool will be applied.
This band however, needed more than just the LRP filter. Noise is also evident throughout the image as well. After applying the LRP filter, an MNFNR filter is needed. Once the MNFNR filter is opened, select TM4 and TM5 as your output parameters. We chose both of these bands because they were the only two bands in the image that were completely clean. For the input parameter, select TM2. Once the appropriate files are selected, you can run the filter.
The final image will have both the LRP and MNFNR filter applied, leaving you with a cleaned image.
TM3 – Red Band
Issue: Noise – Noisy pixels found throughout the image creating a salt and pepper effect. The salt and pepper effect is “interference resulting the coherent integration of the contributions of all the scatterers” (Mather & Koch, 2011, 61).There is also a hazy line that cuts across the bottom left corner that affects the quality of the image. In the uncleaned image, we can see in the parts circled in red that there is some noise in the image.
Solution: MNFNR was used to help reduce any noisy pixels, as well as to remove the hazy line that cuts across the bottom of the map.
Solution Steps: Just like how we fixed TM1, we will do the same for TM3. Using the algorithm librarian, search for MNFNR. Once opened, select TM4 and TM5 as your output parameters. We chose both of these bands because they were the only two bands in the image that were completely clean. For the input parameter, you need to chose the band that you would like to clean. For this first filter, the input parameter was TM3. Once you have selected the appropriate bands, you can run the filter and the outcome will be a cleaned image.
Issue: Noise – Noisy pixels found throughout the image creating a salt and pepper effect. The salt and pepper effect is “interference resulting the coherent integration of the contributions of all the scatterers” (Mather & Koch, 2011, 61).There is also a hazy line that cuts across the bottom left corner that affects the quality of the image. In the uncleaned image, we can see in the parts circled in red that there is some noise in the image.
Solution: MNFNR was used to help reduce any noisy pixels, as well as to remove the hazy line that cuts across the bottom of the map.
Solution Steps: Just like how we fixed TM1, we will do the same for TM3. Using the algorithm librarian, search for MNFNR. Once opened, select TM4 and TM5 as your output parameters. We chose both of these bands because they were the only two bands in the image that were completely clean. For the input parameter, you need to chose the band that you would like to clean. For this first filter, the input parameter was TM3. Once you have selected the appropriate bands, you can run the filter and the outcome will be a cleaned image.