Lab 8: Advanced Classifiers 1

Goal

This lab exercise utilizes robust algorithms which have been developed and tested to improve the accuracy of remotely sensed image classification compared to the traditional methods including supervised and unsupervised classification. Within this lab I learned how to perform a fuzzy classifier to help and solve the common problem of mixed pixels in an image and how to divide mixed pixels into fractional parts in order to perform something called spectral linear unmixing. 

Methods

Linear Spectral Unmixing
In this portion of the lab I used the technique of linear spectral unmixing to produce a subpixel fractional map of my original image. This method is used to produce subpixel images. For this particular lab I used environment for visualizing images (ENVI) software which is similar to ERDAS Imagine in that they are both image processing softwares. To perform the linear mixture model, the 'pure' pixels (endmembers) must have their spectral refelectances measured. Ideally, I would collect ground-based spectra in order to produce these 'pure' pixels because even endmembers taken from high spatial resolution images might have multiple components. Endmembers can also be gathered from high resolution images of specifically the study area however they may require PC (principal component) or MNF (minimum noise fraction) to be used in order to identify the individual 'pure' pixels of multiple surface components.

 (Fig. 1) The starting image/ original image used in this portion of the lab exercise opened in the software ENVI.



  To produce endmembers from an ETM+ image we first must chose the option, transform-principal components-forward PC rotation-compute new statistics and rotate within the ENVI interface. Once the principal component input file window is open I used the original image as the input image and all of the automatic parameters are accepted. Once this and the forward PC parameter models are run, a total of six principle component images for each of the reflective bands which make up the original image will be produced. I then loaded Band 1 of the output image in gray scale (Fig. 2). 

(Fig. 2) This image shows the PC output image in gray scale which was created using the software, ENVI.
 

  Then next step is to view the 2D scatter plots which I used to create the various endmembers for the LULC classes. To do this I had to go back to the original image that I loaded into ENVI and select the tool, 2D Scatter Plot and Scatter Plot Band Choice. For the first plot I chose PC Band 1 for Band X and PC Band 2 for Band Y the resulting graph can be seen in Fig. 2. The next step was to collect the endmembers. To do so, I selected the class-items 1:20 option from the dialog at the top of the scatter plot and first selected the green color. I then drew a circle around the three vertices at the far right end of the scatter plot. This then once I right-clicked inside the circle the areas which were selected on the scatter plot appeared as green on the original map image. I then repeated this using a yellow and blue color. From this process I was able to classify these regions of the image as specific LULC classes (Fig. 3). This included: green-bare soil, yellow-agriculture and blue-water. Next, I did the same process however this time I created a scatter plot with PC Band 3 for Band X and PC Band 4 for Band Y. Then I used the color purple and drew circles in area where I thought I would find urban LULC class (Fig. 4). The next step was then to save the ROI points and create a single file which contains all the endmembers.

(Fig. 3) This 2D scatterplot shows the isolated endmembers created using ENVI. The colors represent the following LULC class: green-bare soil, yellow-agriculture, blue-water.

(Fig. 4) This 2D scatterplot shows the isolated endmember of the urban/built-up LULC class, represented by the color purple.



  After all of this is done it is now time to implement the linear spectral unmixing. To do this I selected the option spectral-mapping methods-linear spectral unmixing and added my original image and my combined ROI file to the dialog. This then created my fractional images (Fig. 5). These show the values of my LULC classes in the red, blue and green bands.

(Fig. 5) Viewer #2 shows reflectance in the red band, which illustrates the bare soil LULC class having greatest reflectance. Viewer #3 shows reflectance in the blue band, which illustrates the water LULC class as having the greatest reflectance. And viewer #4 shows reflectance in the green band, which illustrates the agriculture LULC class as having greatest reflectance because it has healthy, green vegetation.

Fuzzy Classification
Fuzzy classification is used to perform the same task as the linear spectral umixing. The main goal is to correctly identify mixed pixel values when performing accuracy assessments. This method takes into consideration that there are mixed pixels within the image and that it is not possible to perfectly assign mixed pixels to a single land cover category. This particular method however, uses membership grades where pixel value is decided based on whether it is closer to one class compared to the others. There are 2 main steps in the process of performing fuzzy classification: estimation of the fuzzy parameters from training data and a fuzzy partition of spectral space. 

In the first part, I collected training signatures in order to perform the fuzzy classification. I had done this in previous lab exercises (specifically lab 5) however this time I collected samples in areas where there are mixtures of land cover as well as in areas where the land cover is homogeneous. I collected a total of 4 training samples of the LULC (land use/land cover) class water, 4 of the forest class, 6 of agriculture, 6 of urban/built-up and 4 of bare soil. 
  The next step is to actually perform the fuzzy classification. To do this I opened the supervised classification window and selected the option to apply the fuzzy classification and named the output distance file that will be produced. I also made sure that the parametric rule is set to maximum likelihood and the non-parametric rule is set to feature space. Once this model has been run, the next step is to run a fuzzy convolution. This will use the distance file I created in the previous step and produce the final output image (Fig. 6).

(Fig. 6) The image shows the fuzzy convolution image on the left and the final fuzzy classification image on the right.



Results

The process of using the ENVI software to perform linear spectral unmixing was quite tedious at times but did yield very accurate results. I thought that this method was much more accurate in properly classifying pixels to the correct LULC class compared to the fuzzy classification method. This is because the fuzzy classification method classified far more urban/built-up areas than actually exist in the Eau Claire and Chippewa Counties. Because of this exaggeration of the urban areas it incorrectly classified agriculture and bare soil LULC as urban. However, the linear spectral unmixing method was much more accurate as I was able to look at the pixel classification within each band of the image and determine the accuracy of them.

Sources

The data for this lab was gained from the following sources: Landsat satellite images from Earth Resources Observation and Science Center and the United States Geological Survey. All data was provided by Dr. Cyril Wilson of the University of Wisconsin Eau Claire.