Goal and Background
The goal of this lab was (1) to learn how to delineate a study area from a larger satellite image scene, (2) to demonstrate how spatial resolution of images can be optimized for visual interpretation purposes, (3) to introduce some radiometric enhancement techniques in optical images, (4) to learn to link a satellite image to Google Earth which can be a source of ancillary information, and, (5) to become familiar with various methods of resampling satellite images. All of this was done in Erdas Imagine.
Methods
In Erdas Imagine, I first made an inquire box and used the "subset and chip" tool to create an image subset. I did this by specifying an input file, an output file, and selecting "from inquire box" to select the proper area. This image subset is shown in the Results section.
Next I created another image subset via .aoi file. First, I imported a shapefile delineating Eau Claire and Chippewa counties. Next, I selected this area and saved the selection as an area of interest (.aoi) file. I then used the subset and chip tool to create another image subset, this time selecting my area of interest file to be the boundaries. This image subset is shown in the Results section.
Then, I performed image fusion on two images, one panchromatic and one spectral. I used the resolution merge tool under pan sharpen in Erdas Imagine to do so. This involved specifying the panchromatic image, the multispectral image, and the output image. I selected the multiplicative method, and the nearest neighbor sampling method. I then ran the tool, and observed the differences between the original multispectral image and the pan sharpened one. The main difference was in spatial resolution.
Next, I used the haze reduction tool on an image of the Eau Claire area, and noted the differences in the original image, and the haze-reduced image. This tool removed the haze effect and cloud haze from the image.
Next, I connected my Erdas Imagine image viewer to Google Earth. I viewed them side by side, and thought about the potential benefits this could include for image interpretation.
In the next section of the lab, I performed image resampling on a satellite image of the Eau Claire area. I did this using the resample image size tool. I resampled up from 30 pixels to 20 pixels using the nearest neighbor method. I then repeated this process using the Bilinear Interpolation method. Finally I compared the original image to each of the resampled images, noting the smoother properties of the resampled ones.
Next I created another image subset via .aoi file. First, I imported a shapefile delineating Eau Claire and Chippewa counties. Next, I selected this area and saved the selection as an area of interest (.aoi) file. I then used the subset and chip tool to create another image subset, this time selecting my area of interest file to be the boundaries. This image subset is shown in the Results section.
Then, I performed image fusion on two images, one panchromatic and one spectral. I used the resolution merge tool under pan sharpen in Erdas Imagine to do so. This involved specifying the panchromatic image, the multispectral image, and the output image. I selected the multiplicative method, and the nearest neighbor sampling method. I then ran the tool, and observed the differences between the original multispectral image and the pan sharpened one. The main difference was in spatial resolution.
Next, I used the haze reduction tool on an image of the Eau Claire area, and noted the differences in the original image, and the haze-reduced image. This tool removed the haze effect and cloud haze from the image.
Next, I connected my Erdas Imagine image viewer to Google Earth. I viewed them side by side, and thought about the potential benefits this could include for image interpretation.
In the next section of the lab, I performed image resampling on a satellite image of the Eau Claire area. I did this using the resample image size tool. I resampled up from 30 pixels to 20 pixels using the nearest neighbor method. I then repeated this process using the Bilinear Interpolation method. Finally I compared the original image to each of the resampled images, noting the smoother properties of the resampled ones.
Results
Below are the subsetted images described above.
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| This image subset was created using an Area of Interest (.aoi) file. |
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| This image subset was created using an Inquire Box |
Sources
None
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