Programming: Module 1

This week's lab introduced how to use Python IDLE and PythonWin.  It focused on looking at the syntax, and how to write pseudocode code.  Another part of the lab was to practice running scripts.

The script I was created 12 module folders.  Within each folder, 3 sub folders were created: data, results, and scripts.  After running the script, it is clear how much more efficient.

Used Script to Create Folders

To run the script to create all the folders, I found the saved script.  I had to right click and select edit in PythonWin.  PyhtonWin is an IDE that allows for me to run a script in a window and also save thet script in another.  The comments before each section helped explain what the script would do.  I select the "Run" button to run the script and all the folders were created under GIS Programming.  I am interested to learn to build other queries like this to help me in school and work.

Week 10: Supervised Classification

Week 10 objectives are to create spectral signatures and AOI features in ERDAS.  From there, create a newly classified image from a satellite image.  Once the image is recoded, identify and resolve confusion between the signatures.

Create the map below, I started with an image of Germantown, Maryland.  I opened the signature editor and added a new AOI layer.  I used the polygon tool to select features such as water and roads.  I used the inquire tool and coordinates to find urban areas, agriculture, and more.  If the feature seemed to contain a lot of differing pixels, I used the growing properties to select the feature I used the inquire tool for and adjusted the spectral Euclidean Distance. 

Once all my signatures were created, I looked at the different layers to see where confusion existed and to identify the best bands to display.  I used bands R-4, G-5, and B-6.  The final step was to recode all of the signatures.  I was able to calculate the areas of each class within ERDAS.  I was not 100% satisfied I was not able to eliminate all pixel confusion.  I attempted to adjust my signatures and change bands, but did not seem to find success.  I look forward to applying these techniques in through a potential job or practice to improve my skills.

Supervised Classification of Germantown, Maryland using bands R-4, G-5, and B-6

Module 9: Unsupervised Classification

Unsupervised Classification

The goal of the lab was to perform an unsupervised classification using ArcMap and ERDAS.  Another aspect of the lab was to classify images with different spatial and spectral resolution.  The final goal was to learn how to reclassify and recode images in ERDAS.

The lab started off with using the Iso Cluster tool and Maximum Likelihood Classification tool.  Doing this, it created a classified image which I assigned classes colors.

In ERDAS, I used the Unsupervised Classification tool.  This allowed for me to give the image 50 classes.  Once the image had the classes, I reclassified by opening the attribute tables to change the colors of the pixels that belong to each feature.  I did this throughout using the Swipe tool to help identify from the true image.  It was also helpful to change the pixel group to a red or yellow to see how much it is used in the image.  From there, I continued to classify the pixels.

Once the pixels were classified, I used the Merge tool to group the 5 classifications into 5 classes.  I looked at the classes and assigned them groups to bring the classes from 50 to 5. I added the area of the features to help determine how much is impermeable and permeable classes.

Lab 8: Thermal Infrared Images

The objectives of lab 8 is to interpret thermal infrared images using the Stefan Boltzmann constant and create thermal images by adjusting symbology and band combinations. The images that were used were from Ecuador and Pensacola.   

Once I went through the exercises, the last assignment was to identify features applying what I learned and enhance the image.  From the exercises in lab, I noticed there was a visible difference between farm land and natural vegetation when I adjusted the bands to R-6, G-4. B-7.  However, that difference was not as noticeable when I looked at the image with layer 6.  The two different feature types look very similar.  Band 6 shows how the vegetation emits less energy than the urban areas.

Thermal and Multispectral Image of Ecuador

Lab 7: Multispectral Image

The objectives of lab 7 were to look at the histograms of an image and use tools to look at data.  By looking at the histogram, an objective was to interpret the data. Within ERDAS, also use the Inquire Cursor tool to look at pixel data and find tools from the Help Tab.  After going through all the exercises, applied skills to identify features.

By looking at the metadata histograms, I was able to see if the features I was looking for were dark or light.  The size of the spike also told me if the feature was large or small.  Once I had an idea of the brightness and size, I used the inquire cursor to see pixel count.  I was then able to see the pixel count for each layer and see if it matched the clues.  I then adjusted the color to spectrum to help highlight the features I needed to find.

Deep Water - TM False Natural Color

Snow - True Color

Shallow Water - Near Infrared Color

Lab 6: Image Enhancement

Enhanced Image

This week's lab objectives were to download satellite images from USGS.  Once the data was downloaded, perform spatial enhancement in ArcMap and ERDAS.  The enhancements included high and low pass filters and Fourier Analysis.

To enhance the striped image, I used the Fourier Analysis tool to fill in the striped areas.  By using the Wedge tool and Low Pass button.  Once this was done, I sharpened the image with kernel size 3x3.  After sharpening the image, I used the convolution tool to apply a 3x3 low pass filter.  Once that filter was applied, I moved the image to ArcMap where I adjusted the histogram.

I enjoyed this lab because it was interesting to see how images can be improved and by various tools.  However, it was challenging to figure out how to continue to improve the image.  I did play around with various filters and tools.  It is clear a lot of experience and practice is needed to develop image enhancement skills.

Remote Sensing: ERDAS Imagine

Lab 5a objectives were to calculate wavelength, energy of EMR, and frequency.  The other part of the lab was to learn how to use ERDAS Imagine and view the data with the Viewer.  The final part of the lab was to take subset data and prepare a map.

The first part of the lab compared the relationship between wavelength and frequency.  The calculation compared wavelength to energy.

Once in ERDAS Image, I played around with various tools such as zoom and pan.  It was also important to go through the different options for the raster after adding it to the table of contents.  After adjusting options. added a Viewer #2.  After having the image of Washington, I adjusted the color bands to show the forest land.

Another tool I learned was adding a column to an attribute table.  This helped show the power of ERDAS Image and exporting to ArcMap.  Using the Inquire Box tool from the Home tab allowed me to select an area of the Washington State image and then subset created the smaller image.  Exporting this into ArcMap allowed me to change the symbology to show the different classes and then prepare a map.

ERDAS Image and Classification