Remote Sensing Lab 4

Goal and Background: The purpose of this lab is to use Erdas Imagine to be able to create a specific area from a larger satellite image, be able to optimize spatial resolution of images for visual interpretation, successfully link to Google Earth to obtain ancillary information, and to be introduced to various satellite image resampling methods, using image mosaicking and binary change detection.

Methods:

PART 1 - Image Subsetting

Section 1: Subsetting with the use of an inquire box
I first opened Erdas Image Viewer. I then opened a TM image that was given to me by my professor. I implemented the raster tool and then right-clicked an area on the image to select the 'inquire box'. I used the inquire to create an study area the encompassed all of the Eau Claire and Chippewa area. I then used the 'Subset & Chip' and 'Create Subset Image' tools to create my subset image which needed to be saved into as an output file. After saving the image, I clicked on the button 'from inquire box' to bring the coordinates covered by the inquire box into the subset interface. After this ran, my subset image had been created (Fig. 1)

Section 2: Subsetting with the use of an area of interest (AOI) shape file
In this section I used the same TM file as I did in section 1. I then added a shapefile of Eau Claire and Chippewa counties to the viewer with my input image. In order to see the shape file I had to change the file type from image (.img) to a shape file (.shp). After I did that, the shape file overlaid the TM image. I then created an AOI around the shapefile by holding down the shift key and clicking on the two counties respectively. Next I clicked on 'paste from selected object' in the toolbar which then created dotted lines and the area now became an AOI. I then saved the new AOI file and employed the 'raster and subset & chip' as I did in section 1 and used the new AOI file to create my subset image (Fig. 2)

PART 2 - Image Fusion

The goal here was to increase the spatial resolution of a coarse resolution image with the use of another image. I used the raster 'pan sharpen' and 'resolution merge' tools to fuse the two images. I input both the panchromatic band image and the multispectral band image into the tool. I then used 'nearest neighbor' where I then opened the metadata of the original image to observe the pixel size. Then, I used image fusion and resampling to resample the image while pan-sharpening it.

PART 3 - Simple Radiometric Enhancement Techniques

I was given an image with major haze issues which needed to be corrected. I used the 'haze reduction' raster tool to reduce the haze and clouds in the image.

PART 4 - Linking Image Viewer to Google Earth

This section aimed to introduce the ability to synchronize Google Earth in Erdas with an image to be analyzed. I then linked and synchronized my view screen and Google Earth to be able to zoom in and out with both images.

PART 5 - Resampling
 
Here I used the original image provided and used to resampling techniques to compare the outcomes. After noting the pixel size, I opened the raster tools and used the spatial, resample pixel size on the input image. I first used the nearest neighbor technique and then used the same process but with bilinear interpolation to observe the differences between the two techniques. Both times I resampled the image from 30x30 meters to 15x15 meters and made sure the pixel sizes remained square.

PART 6 - Image Mosaicking

Image mosaicking is helpful when a study area is larger than the special extent of one satellite image scene, or where the AOI is relatively small but covers the portion that is at the intersection of two adjacent satellite images. I imported my input images, making sure that Multiple Images in Virtual Mosaic and Background Transparent were checked.

Section 1: Image Mosaic with the Use of Mosaic Express
I used the raster mosaic tool, Mosaic Express. I input my two images in the correct order and then ran it. Creating a new image, I could see that this was not entirely desirable as there was a clear distinction between the two images.

Section 2: Image Mosaic with the Use of MosaicPro
MosaicPro is basically just an advanced version of mosaicking. I added the two input images into the tool and made sure that Compute Active Area was set as the default. I adjusted the radiometric properties by using the Color Corrections and Use Histogram Matching for color corrections and used Overlap Areas to match the histograms to the overlapping areas to preserve color and brightness values. I then ran the mosaic.

PART 7 - Binary Change Detection

Here I learned about binary change detection and image differencing.

Section 1: Creating a Difference Image
Inputting a 2011 and a 1991 multispectral image of the Chippewa Valley area through using Two Image Functions, the interface was used to subtract the 1991 image from the 2011 image. Determining the cutoff points for the values that have changed between those years, from the histogram and metadata, I used the equation of mean + 1.5 multiplied by the standard deviation based on the Gaussian distribution shown in the histogram.

Section 2: Mapping Change Pixels in Difference Image Using Spatial Modeler
This was a map of changes of the Eau Claire County area during the same time frame as section 1. I used the following equation to create a model that would remove the negative values from my difference image brightness value:
ΔBVijk = BVijk(1) – BVijk(2) + c 

Where:
ΔBVijk = Change pixel values. 

ΔBVijk(1)= Brightness values of 2011 image. 

BVijk(2) = Brightness values of 1991 image. 

c = constant: 127 in this case. 

I = line number 

J = column number 

K= a single band of Landsat TM.

 Results:

Fig. 1

 

Fig. 2

 

 

Fig. 3: This image shows the result from Part 2 with the resulting pansharpened image.

 

 

Fig. 4: This image shows the results from Part 6 section 1 after using Mosaic Express

 

Sources:

All images and data were provided by Dr. Cyril Wilson in Lab 4.