Lab 6

This lab focused on two geometric correction methods: Image-to-map rectification using nearest neighbor resampling and image-to-image registration using bilinear interpolation resampling.

Image-To-Map Rectification:

I used a map of Chicago to collect Ground Contrtol Points (GCPs) for a satellite image of Chicago. The rectification was set to a first order polynomial and I collected 4 GCPs and aqcuired a RMS of 0.2 and resampled the image using nearest neighbor.





Image-To-Image Registration:

I used a reference image of Sierra Leon to collect 12 GCPs for a third order polynomial registration and achieved a RMS of 0.07. I then resampled the image using bilinear interpolation. The finished product had a white, hazey appearence, but the features in the images aligned well with the reference image.

Lab 5

The goal of this lab was to become acquainted  with the processes of image mosaicking, band ratioing, and conducting spectral or spatial image enhancement. These processes are important when faced with a situation in which you must collect data from an area that spans across multiple images or you must analyze and interpret the data of an image that may not be clear.

Two image mosaic processes were used and compared against each other to become familiar with the function and to see which process was more useful when stitching images together.

MosaicPro:

I added the images of interest into the MosaicPro tool and selected "Compute Active Area" in the Image Area Options menu. I used "Histogram Matching" to correct the color in order to make a more seamless stitch in the overlapping area. The Overlap Function perameters were set to default in Overlay to set the brightness values to the top image's in the area where they overlap. The end product was more of an unnoticeable seam than the one mosaic made using MosaicExpress. This is because MosaicPro allows you to manipulate more aspects of the mosaicking process.

MosaicExpress:

I experimented with the MosaicExpress tool to create a mosaicked image of the Eau Claire/Chippewa Valley region. This tool successfully created a mosaic without much effort, but because it did not allow you to correct the colors or choose brightness values at the overlay area, the mosaic has a noticeable overlay region and the images do not blend together.

Band Ratioing:

NDVI (Normalized Difference Vegetation Index: NDVI=(NIR-Red)/(NIR+Red)) was performed on an image of Eau Claire in 2011. White portions are a higher value that indicate a higher difference between the NIR and red bands because of differential absorption and reflection. This represents healthy vegetation because photosynthesizers absorb the red light and reflect much of the NIR.

Image Diferencing to Detect Change:

I assessed the pixel differences between 1991 and 2011 in Eau Claire by differencing the images with the Two Input Operators tool in the Two Image Functions option. Only layer 4 was processed. This image's histogram was stretched to be able to incorporate more brightness values in the image and the upper and lower change-no-change threshold values were used to calculate the difference between Eau Claire in 1991 and 2011 in  ModelMaker.

Lab 4

Goals: 

The purpose of this lab exercise was to develop skills in image enhancement and pre-processing techniques as well as the methods for delineation of areas of interest. This was achieved by sub-setting images through use of inquiry boxes and area of interest tools, altering spatial resolution of images for ease of interpretation and analysis, enhancing radiometric quality of images, linking Google Earth and Erdas 2013 viewers in order to understand features with the use of Google Earth as a selective interpretation key, and applying re-sampling methods.

Methods: 

Using inquiry box tools and the delineation of areas of interests in a shapefile, I created smaller subsets from satellite images. To enhance the spatial resolution of satellite images, I pan-sharpened the image using resolution merge. For further understanding of image enhancement for analysis purposes, I resampled up by first using the nearest neighbor method and then using the bilinear interpolation method and increased spatial resolution by reducing pixel size from 30m x 30m to 20m x 20m. I used radiometric enhancement to reduce haze on another image using the "haze reduction" tool on Erdas 2013. 

Results: 

Pansharpened Image with Resolution Merging  Original image is displayed on the left; pansharpened image is displayed on the right

Resampled Images Using Different Methods

Resampled images using nearest neighbor (top right) and bilinear interpolation (bottom right). The original image is displayed on the left. 

Haze Reduction via Radiometric Tools

Original image (left), Reduced haze image (right).