Lab 4 - Pixel-Based Supervised Classification

Goal and Background

The main goal of this lab was to develop an understanding of conducting a pixel-based supervised classification scheme to classify 5 different Land Use Land Cover (LULC) classes.  The first section of this lab was dedicated to the collection of training samples for a variety of surface features.  the second portion of this lab was dedicated to the evaluation of the quality of the training samples collected.  Finally, the third and final section of this lab was dedicated to the actual production of meaningful LULC classes.

Methods

Part 1:  Collection of training samples for supervised classification

to begin this lab, our first task was to go about selecting training samples, or samples of known LULC, that can be used to train the classifier.  To do this, we used the Raster>Unsupervised>Signature Editor window as well as the Draw>Polygons tool.  For each training sample collected, we created a polygon within the specific LULC feature and then clicked the Create New Signature From AOI.  Training samples were first collected for waster, then forest, then agriculture, then urban/built-up, and then finally for bare soil.  Once all of these training samples were collected, the samples were labeled and numbered with their respective LULC.  The signature file was then saved once all training samples were collected and correctly labeled.

Part 2: Evaluating the quality of training samples

The next step of this lab was to check to make sure the training samples we collected in the previous part of the lab were of sufficient quality.  To do this, we displayed the signatures of each training sample in the Signature Mean Plot window which was accessed from the signature editor window.  Next, we made sure to accept the default values for the Image Alarm and the Parallelepiped Limits.  Next, we viewed the histogram for our data by clicking the histogram symbol and making sure that Single Signature was selected under the Histogram Plot Control Panel.  This was done for each of our individual LULC classes so that we could compare the signatures of the samples we selected to the real world signatures of those same things.  Next, a separability report was created by clicking Evaluate>Separability on the Signature Editor window.  Once the Signature Separability window was open, we changed the Layers Per Combination to 4 and chose Transformed Divergence for the distance measurement.  Once this was done, the separability report was created which was used to evaluate the separability between spectral signatures for each band so we knew which bands were the best to use for our supervised classification.  Once this was completed, all of the individual spectral signatures were merged into one signature for each of the LULC classes using the Edit>Merge tool within the Signature Editor window.

Part 3:  Performing supervised classification

The final portion of this lab was all about taking all the information obtained and created in the first two parts and using it to actually perform a supervised classification.  To do this,we clicked on Raster>Supervised>Supervised Classification and set the input image to the provided image of Eau Claire and Chippewa counties and then set he input signature as the merged signatures created from the previous part of this lab.  We then made sure that the Non-Parametric rule was set to None and that the Parametric Rule was set to Maximum Likelihood.  Finally, the classification tool was run and the image was brought into a viewer for examination. The Supervised Classification image was then compared to the Unsupervised Classification image created in the previous lab and finally a map was created using the Supervised Classification image.

Results

Figure 1. - Spectral Signatures of all 50 Samples

 Figure 2. - Merged Spectral Signatures

Figure 3. 

Sources

Data sources are as follows: Landsat satellite image is from Earth Resources Observation and Science Center, United States Geological Survey.