Remote Sensing Lab 4

1. Background and Goals:

The goal of this lab was to learn how to create a subset of an image to isolate a study area and how to perform miscellaneous images functions. These include image fusion, radiometric enhancement, creating an image mosaic, re sampling, and linking a viewer with Google Earth. These functions were performed using Erdas Imagine 2016 software.

2. Methods and results

2.1- Image Sub Setting

 The first task of this lab was to create an image subset to highlight an area of interest. For this example the image used was a satellite photo of western Wisconsin and the area of interest was the Eau Claire/Chippewa Falls metro area. To create an image subset, the inquiry box is used to highlight the area of interest. Next, using the subset and chip tool under the raster functions, a subset image may be created and saved. The  subset image may be seen below in figure 1.

Figure 1.
Subset image showing Eau Claire/Chippewa falls Metro area

This same process can be done by overlaying a shapefile on an image, selecting
the shapefile and clicking paste from selected object and saving it as an area of interest file.

2.2  Image Fusion

The goal for this section of the lab was to change the spatial resolution of an image using image fusion techniques. A 15 meter panchromatic image was used to "pan sharpen" a 30 meter reflective image. Under the raster tools in Erdas Imagine, the pan sharpen and more specifically resolution merge tools were used. The method used was multiplicative and the nearest neighbor re-sampling technique was used. The results can be seen below in figure 2

Figure 2.1
Image before being Pan-Sharpened. 

Figure 2.2
Image after being Pan-Sharpened   

2.3 Radiometric Enhancement

This portion of the assignment focuses on radiometric enhancement and more specifically haze reduction. Under the raster toolbar in the radiometric toolset is the haze reduction tool. This was used to reduce haze in a 2007 satellite image of the Eau Claire, Wisconsin area. The before and after images can be seen below in figure 3. Changes are most noticeable over bodies of water and in the lower right hand corner of both images. 

Figure 3.2
Image after haze reduction

Figure 3.1
Image before haze reduction

2.4 Using Google Earth with Erdas Imagine 2016

One feature of the Erdas Imagine software is the ability to synchronize a viewer with google earth. This can be done by selecting the google earth toolbar and and using the connect to google earth tool. Using the link tools, the viewer can be linked with the view in google earth, and vice versa.

2.5 Re-sampling 

Re-sampling is the process of changing the pixel size of an image. This can be done in Erdas Imagine by using the re-sample pixel tool in the spatial toolset under the raster toolbar. In the tool, the new pixel size can be determined and the re-sampling method can be selected. For the purposes of this lab, 30x30 pixel size was re-sampled to a 15x15 meter size using both nearest neighbor and bilinear convolution methods.

2.6 Image Mosaic.

An image mosaic is created when a study area is larger than a single image and two images must be combined into one. This can be done using either mosaic express or mosaic pro within Erdas Imagine. First,  the image must be highlighted in the select layers to add window before being added to the viewer. Then the multiple option must be selected as well as the multiple images in a virtual mosaic. Under the raster options in the same dialog, the background transparent and fit to frame tabs must be checked.

For this lab, two images were placed in an image mosaic using both mosaic express and mosaic pro.
In the mosaic express tool, the images were placed in the desired order and the default parameters were used. The resulting image can be seen below in figure 4.

Figure 4.
Mosaic Express image

Because a better quality image was desired, the same process was done using mosaic pro.
The images were positioned in the correct order in the mosaic pro window. To make the transition between the images more smooth, the color correction tools were used. The histogram matching option was selected and the overlap areas were selected under "set" as the matching method. This matched the brightness values of the images close to the intersection point. This creates a much smoother transition between images.  The results can be seen below in figure 5.

Figure 5
Mosaic Pro image

2.7 Binary Change Detection and Image Differencing 

The final portion of the lab focuses on creating a difference image and mapping changes in brightness values. In the functions toolset under the raster toolbar, the two image functions tool was selected. The two images that were to be differenced were inputed and the subtraction operator was selected. Using the differenced image, the histogram was studied to determine which pixel values indicated change. This is calculated by multiplying the standard deviation of the histogram by 1.5x and either adding or subtracting the result from the mean to find the upper or lower boundary. In this instance, pixels on the histogram with brightness values above 73.25 or below -23 represent pixels that have changed. 

The second part of this section focused on mapping the changed pixels using spatial modeler. For this a new difference image was created. In spatial modeler window, two input raster objects were put into place and the two images being differenced uploaded. A function object was added and a function to subtract the image pixel values from each other was entered. A constant of 127 was also added to avoid any negative values. 

The new differenced image was placed into a new model. A new threshold was calculated using mean + (3x standard deviation). A function was than added to the modeler that assigned all pixel values below this calculated threshold a value of 0 and all others a value of 1. By placing this image in arc-map, these values can be symbolized to represent pixel values that have changed. In example can be seen below in figure six. 

Figure 6
Map showing change in pixel brightness values

3. Data Sources

Satellite Images-  Earth Resources Observation and Science Center, United States Geological Survey.
Shape Files-  Mastering ArcGIS 6th edition Dataset. 2014 Maribeth Price, McGraw Hill