Lab 4: Introduction to Erdas Imagine

The first 3 labs of this course were devoted to learning the most basic functions of Erdas Imagine. For lab four I was introduced to some of the more complex and interesting functions which this program is capable of. For example I was required to delineate an area of interest from a large satellite image, manipulate images to enhance their ability to be analyzed, and used graphical modeling to distinguish areas of an image where changes in vegetation have occurred. 

Objective 1:  Image Subsetting

                This objective of the lab involves subsetting an image by the use of the subset and chip function of Erdas Imagine. The first step to using this function is defining an area of interest (AOI) from an Image. There are two simple ways that an AOI can be determined: by using an inquire box (figure 1) or by using a georeferenced shapefile (figure 2). While using the inquire box function is simple the shapfile AOI selection method is advantageous because it can display predetermined boundaries easier and is not limited to rectangular shapes. 

Figure 1. Left: original Image. Right: Inquire box subsetted image. 

Figure 2. Left: original image, note polygons over Chippewa and Eau Claire counties. Right: shapefile defined subsetted image. 

Objective 2: Image fusion

                For this objective I was able to improve the spatial resolution of an image by merging a 30 meter reflective image with a 15 meter panchromatic image. The pixel values were determined by the Erdas resolution merge function set to use a multiplicative algorithm and nearest neighbor resampling technique. One can see the differences between all three images below (Figure 3).

Figure 3. Left: resolution merged (Pansharpened) image. Center: original Multispectral Image. Right: Original panchromatic image.

Objective 3: Basic Radiometric Enhancement Techniques

                With this portion of the lab I used a haze reduction function on Erdas to improve the spectral and radiometric clarity of the image. One can see the improvement of contrast between both images below (Figure 4).

Figure 4. Left: original image. Right: Modified, haze reduced image.

 Objective 5: Resampling

                For this objective I was able to resample the image using both nearest neighbor and bilinear interpolation. There is no difference, aside from the increased amount of pixels, between the original image and the one resampled using the nearest neighbor method. There was a much more pronounced difference between the image resampled using the bilinear interpolation technique and the original. The image is more spatially accurate and many features are smoothed out. One can observe these differences below (figure 5).

Figure 5. Left: original image. Center: nearest neighbor resampled image. Right: bilinear interpolation resampled image. 

Objective 6: Image Mosaicking

                For this objective I was tasked with creating several image mosaics. The first mosaics was created using the Erdas function Mosaic Express. While this technique of image merging was easy to set up and quick to render on the computer the final image left much to be desired (Figure 6). There was not a smooth transition between the images making it quite obvious where the original image’s borders were. One advantage of the function is how the radiometric changes increased the contrast of the output image.  

Figure 6. Left: original images. Right: Erdas Mosiac Express image. 

Objective 7: Image differencing

                This section of the lab was designed to introduce students to the concept of image differencing, a technique used to see changes in one place over time. After opening two images of the same area that were taken 20 years apart I used the Erdas Two Image Functions to perform a binary change detection between band 4 on the images. After running the process no change was apparent in the new image so I was required to manipulate the histogram portion of the image metadata so as to determine the areas brightness changed the most (Figure 8). 

Figure 8. Binary change image histogram.

 In order to better demonstrate the change between the images I was tasked to use Erdas Model Maker to put together a basic model for determining the change in brightness vales between the images. After running a two basic operations with the model maker I was able to isolate the pixels in the image which had changed the most (Figure 9).  

Figure 9. Left: subtracted values between 1991 and 2011 images. Right: Highlighting of pixels that had the most change in brightness values.

 The final operation for this lab is to display the brightness values from the last step on a contrasting base map. I used ESRI ArcMap to complete this task. Unfortunately due to time constraints I was not able to get it to look as nice as I would have liked to (Figure 10).

Figure 10. Completed map showing binary change.