Lab 1

Lab 4

Goal:

The goal of this lab is to use Erdas Imagine to properly subset an area of interest, create a higher spatial resolution for an image, reduce haze in an image, link an image viewer to Google Earth, mosaic images and detect binary changes.

Methods:

Part 1: Subsetting with the use of an inquire box

In this section two methods are used to delineate an area of interest (AOI). Image eau_claire_2011.img was opened in Erdas Imagine. Next an Inquire Box was opened under raster tools. This inquire box was placed over the Eau Claire/ Chippewa area and a subset image was created and saved in my personal lab 4 folder.

Above is the subsetted area of the Eau Claire/Chippewa area. This next method is more commonly used for areas that are not perfectly square or rectangular.

 Result: 

By adding a shape file of the AOI it provides a more exact spot. The shape file is georeferenced so that it covers the exact intended AOI and because the image is subsetted, it is in the same shape as the Chippewa and Eau Claire counties.

Part 2: Image fusion

In this section, a higher spatial resolution image will be created from a course resolution image by using a panchromatic version of that image. By merging the resolutions, a clearer picture can be made of the first image.

Result:

The Panchromatic image is of 15 meter resolution and the Reflective image is 30 meter resolution. When merging the images, the Multiplicative method was used and the Nearest Neighbor resampling technique was used. Although it is not immediately clear from the image above, the image on the left has a higher spatial resolution than the image on the right. It is more obvious when zoomed in.

Part 3: Simple radiometric enhancement techniques

This part of the lab dealt with reducing haze in one image.

Result:

The process of reducing the haze of the image (left) was simply to use the haze reduction tool in Erdas Imagine. The output image is on the right and is much clearer than the original image.

Part 4: Linking image viewer to Google Earth

Linking a viewer to Google Earth can be advantageous for collecting training data for image classification for recent images. Google Earth uses a high resolution satellite image that can be useful when interpreting one’s own image.

Results:

By simply uploading an image of Eau Claire and connecting to Google Earth it is easy to find the same place on Google Earth. Clicking Match GE to View will find the exact location in the image. From there it is easy to analyze and make comparisons by linking and syncing the two views.

Part 5: Resampling

In this part an image was resampled, which means that its pixel size was changed so that the pixel size was reduced (resampled up). Two methods of resampling were used; nearest neighbor and bilinear interpolation.

Results:

The above image shows bilinear interpolation (right). This image has a resolution of 15x15m, while the original image (left) has a resolution of 30x30m.

Part Six: Image Mosaicking

Image mosaicking is used when a study area is larger than a single satellite image. When there are more than one images that intersect, but cover the study area they can be combined together to form a single satellite image. There are a few was this process can be carried out in Erdas Imagine.

Section One: Mosaic Express

Mosaic Express is an easy way to mosaic images, but the outcome may not be as good.

Section Two: MosaicPro

MosaicPro is a more sophisticated way to mosaic images. In this mosaicked image the color flows better between the images.

Results:

MosaicPro takes longer to create a mosaicked image, but it produces a better result.

Part 7: Binary change detection

In this section, two different images of the same area of Eau Claire County are looked at, but the images were taken at different times. By looking at the change in brightness values of pixels, one can detect changes in the landscape.

Section 1: Creating a difference image

Image differencing is one way to detect binary change. In this section only one pair of layers (4) is going to be processed. After combining the images, looking at the histogram is the best way to detect change. Since the areas of change are normally located at the tail of the histogram the tails need to be cut off. In determining the cutoff point for this histogram the rule of thumb threshold of mean+1.5 standard deviation was used.

The cutoff value turned out to be 72.3766.

Section Two: Mapping pixel change using a spatial modeler

Using a spatial modeler, two images the 1991 image was subtracted from the 2011 image to create a new image. In this new image a threshold of mean+(3xstandard deviation) was used to determine the change/no change threshold. Another spatial model was created in order to create an image that shows all the pixels above the change/no change threshold that was just calculated. The image that is created from this model shows only the areas in Eau Claire county that were changed, which makes it difficult to read.

By overlaying the ec_91-11bvis image over the original 1991 image the change in pixel can be detected. The portions in green in the image below shows the change in pixels between the images.

Results: