GIS4035 - Lab 10 - Supervised Image Classification

Well, I finished off the kids' halloween chocolate while working on this final lab assignment; new proof that eating chocolate can reduce daily stress.   Module 10 was a continuation of last week's digital classification of images using ERDAS Imagine.   We performed three exercises prior to creating our final deliverable which was to create a supervised classification of land use consumption of Germantown, Maryland.

First we learned how to create or append signature files by drawing polygons around clusters of like pixels or by growing a signature from "seed" using the Drawing tab/Growing Properties dialog.  Adjusting the Spectral Euclidean Distance (SED) and/or changing the Neighborhood setting from 4-way to 8-way will adjust how a "seed" grows.  Next, we learned how to evaluate the signatures.  Using histograms and mean plots we could view the bands where overlap occurred between two or more signatures.  Determining which bands showed the greatest difference between signatures was important because those bands were chosen to Set Signature Colors in the Signature Editor.  Once colors were set, we could perform a supervised classification of our image using our signatures to "train" the classification.  Using the Maximum Likelihood Parametric Rule, pixels were classed based on the probability that a pixel belonged to a particular signature/class.  A distance output file was also created.  This file was another analysis tool.  Areas that appear very bright in the distance file indicate greater spectral difference which may predict misclassification.  Additional signatures could be added or existing signatures could be modified/replaced to include a larger pixel count.  Once the final supervised image was obtained, the classes could be recoded to merge together "like" classes.   The recoded image requires class names to be recreated; additionally, area calculations can be performed.

For our final deliverable we had to create a signature file using a new AOI layer and the Inquire button to either draw or "seed" polygons around AOIs.  I primarily used the drawing tool but a few times used the seed method.  I created three water body signatures and two road signatures.  Using an 8-way neighborhood setting and a SED value of 7 and 9 respectively I was able to create my road signatures.  After viewing and comparing histograms and mean plots for all signatures I determined that bands 4, 5, and 3 had the greatest separation between the signatures.  Using this R4,G5,B3 band combination I set the signature colors.  The first supervised classification I performed used the Maximum Likelihood Parametric Rule to determine which pixels were assigned to each specific class.  The classified image appeared fine in the viewer, but the distance image showed several bright areas indicating some pixels were misclassified.  I went back to my signature file and noticed that some signatures had pixel counts that were 10 or less.  I modified these polygons and replaced them in the signature editor.  I performed the supervised classification again and then recoded the image.  I noticed my colors did not appear correctly in my recoded image.   I went back again to my signature file and noticed my Deciduous Forest signature was out of place/order.  The Order and Value column values did not match in the Signature Editor.  I wasn't sure how I messed the Deciduous Forest signature up but I edited the Order values to match the Value column and then sorted the signatures based on their Order value.   This made things appear as they should except my Class # was still incorrect for Deciduous Forest (Class 8 instead of 5).  I could not figure out how to fix the Class #.  I recoded the image but again noticed my colors were not matching the 453 combination of the original supervised image.  I recoded the image again but this time in the Setup Recode dialog I assigned New Values based on the Old Values of each class category.  Instead of assigning new values of 1-8 as I had previously done, the new values were 3-Urban, 4-Grasses, 5-Deciduous Forest, 6-Mixed Forest, 7-Fallow, 10-Agriculture, 13-Water, and 16-Roads.   This recode setup did the trick and my image appeared with the classes distinguishable with 453 band combination colors.  Once I added the image to ArcMap I chose to change the symbology of each class to more clearly identify the type of land use/land cover.  This was a challenging lab as it was important to know the purpose of each step and how it affected the image output as well as to stay on top of where files were being saved.   This lab was most useful in learning about and understanding the Signature Editor.

Supervised Classification of Germantown, Maryland using ERDAS Imagine

GIS4035 - Lab9 - Unsupervised Classification

In Module 9 we learned about using ERDAS Imagine and ArcMap to perform unsupervised automated classifications.   In a previous labs, Modules 3 and 4, we had learned about manual land use land cover classifications.   Exercise 1 had us use the ArcMap Iso Cluster and Maximum Likelihood Classification tools which are part of the Spatial Analyst toolset.  

Exercise 2 required us to use ERDAS Imagine to classify a high resolution image of UWF’s campus.  The goal was to reclassify all fifty categories of the original image into 5 feature classes.  Using the ERDAS Imagine attribute table we zoomed into the image to view pixels of several features that were clearly visible.   Next, we changed the color of that feature in the attribute table to one of the specified four categories: Trees/Dark Green, Grass/Green, Buildings/Grey, Shadows/Black.  Using the Swipe/Toggle/Blend tools under the Home tab/View Group we could compare our classification category assignments to the original image.  Changing the color of one feature to make it stand out helped tremendously in assigning class categories.  Some features were difficult to classify because the pixels were split between impermeable and permeable objects.  For some pixels it was difficult to tell whether it represented a roadway, barren land, or grass.   The assigned pixel color might indicate any of those items.  Shadows on building rooftops were really part of the roof but also a shadow creating a darker pixel value than the other rooftop pixels.   Creating a “mixed” category class was needed for those pixels that were split among the impermeable and permeable classes of buildings/roads, grass, trees, shadows.

It was necessary to “recode” the image using the Raster tab/Thematic button and select Recode.  This is different than the Thematic tab/recode button.   The Recode dialog box enables you to sort columns in alphabetical order and create a New Value for each feature row.  After the recode we had to go back and add the Class Names column and add an Area column.

Lastly we calculated the total surface area of the image by adding the individual classification area values.  We had to determine the percentage of land surface that was permeable and impermeable.   In dividing the shadow and mixed categories between permeable and impermeable I chose to split their acreage 50/50.   Some shadows of buildings for example were fairly large in comparison to shadows of trees.  However, there were more trees with shadows than buildings with shadows.   It was hard to determine the exact split so I took the conservative approach and split it evenly.   I used the same assumption for the Mixed class.

Unsupervised Classification of the University of West Florida Campus