GIS4035 - Module 6 Lab - Spatial Enhancement

In this first lab for Spatial Enhancements we learned various filtering methods that can be used to enhance an image depending on the analysis and interpretation needs.   The different exercises and tasks demonstrated how to use a variety of methods both in ERDAS and in ArcMap.

Our First Exercise required us to learn how to download imagery data.  We were provided a USGS website and a specific sensor.   We changed the location to center the viewer on Pensacola Bay and selected a specified image date (Julian Date).   The name of the image is a description of the sensor type, location and date of the data.  Once downloaded the data needs to be imported into ERDAS and converted into an image file.

In the Second Exercise ERDAS Convolution tools under the Raster, Spatial menu, were used to create two basic enhanced images; a low pass filtered image and a high pass filtered image.  Low pass filters “smooth” out the data by eliminating erroneous pixels or unnecessary small features.  The high pass kernel type highlights small scale differences or high levels of changes in cell values from cell to cell, eliminating broad patterns but highlighting “edges” or feature types such as roads or water bodies.

We also used Focal Statistics in ArcMap to apply statistical filters to an image.  Focal Stats performs various statistical tests using an adjustable kernel of various shapes and sizes.  New values are calculated for each cell in the raster.   The lab had us create a Rectangular Mean 7x7 filter which is a low pass filter.  A larger kernel size results in each new cell value being the average of a larger area so the output image will be more generalized.  The second filter we applied to the original image was an Edge Detect filter using the Range statistic.  This filter type gives each new cell a value that reflects the difference in brightness between the neighboring pixels which can help reveal the borders between different types of features. 

In Exercise Three we used ERDAS to perform Fourier Analysis to diminish the effects of striping bands that can appear in an image due to sensor malfunction which causes frequent lines of no data.  This method is under the Raster tab, Scientific group, Fourier Analysis button.  You first create a Fourier Transform file and then use it as the input to create an image file.  The Fourier Transform Editor has various tool buttons that can be used to enhance the image.   Low and High Pass filters and various Masks.   We used the Wedge Mask and Low Pass filter to diminish the striping effect.

We further enhanced the image by using the Sharpen tool in the Convolution toolset.  The Fourier image file previously created was used as the input file.  The 3x3 Sharpen filter is a high pass filter that sharpens edges but does not reduce overall details in the image.  Different kernel methods can be used to filter images.  I found this filter selection guide that provides a description for various filters.

https://hexagongeospatial.fluidtopics.net/book#!book;uri=5d68e1db557af5bab494d96c7f8e1a9d;breadcrumb=98d11f6e256a86a22eaebb041a3f6075-e2158ff6602259c9651bd3ed7f08262e-5b95c34a774882c13680195f5cc2e0fb-0d93bff57e8bbede8d9c7bd229e8a569

My final deliverable, Task 3 for Exercise 3 started from scratch using the original striped image (l7_striping.img).  First, I performed a Fourier Transform on the image.   This was my third time using this analysis technique in ERDAS and I was able to create a better fft file than my first two attempts from the lab because I utilized the Wedge and Low Pass buttons more effectively when dragging the cursor.

My output Fourier3.fft was used to create my Fourier3.img file.  This Fourier image was used as the input file for Convolution Filtering.   I first applied an Edge Enhanced 3x3 Kernel and created edgen3x3_3.img.  I read using the ERDAS Help that Edge Enhance brings out the edges between homogeneous groups of pixels, unlike edge detectors (such as zero-sum kernels), they highlight edges and do not necessarily eliminate other features.  (ERDAS)  I didn’t want to lose any features in the image so I felt this was a good kernel to start with.  When I compared this Edge Enhanced image to the original, it appeared a little fuzzy, so I chose to apply another Convolution Filter, Sharpen 3x3 to slightly sharpen some of the edges (sharpedgeen3.img).  The northwest corner to the bottom of this sharpened image had a very dark appearance and I needed to “subtract” some darkness from those objects/features.   I read up on using the Breakpoints Editor in ERDAS but did not feel comfortable using it to adjust the image.   Instead, I chose to use the Brightness Contrast under the Panchromatic tab to brighten the final image.  I felt this enabled the various features in the image to be more visible.

Once I opened the final enhanced image (brisharpenedgeen3.img) in ArcMap I wanted to show the original image contrasted with the enhanced image.   I chose to display three data frames.   The first data frame displays the full extent enhanced image as required for the lab deliverable.   The other two frames are focused on a section of the image I referred to as “mama bear”.  This side by side comparison I felt showed the effects of the different enhancement techniques I used on the original image. I had difficulty displaying the original image in both Data and Layout views.   I could not display the full extent of the original image.   In retrospect I should have shown the “study area” on the full extent frame of the enhanced image but I did not go back and try to mess with it. 

This was another challenging lab. I vaguely remember using the Fourier Theory in Electrical Engineering courses but it was so long ago and that knowledge was never applied during my career experiences.  There clearly is so much material involved in remote sensing and we are just scanning the surface in these labs. 

Image Enhancement of Pensacola Bay Using Fourier Analysis and Convolution Tools in ERDAS