Lab 8: Advanced Classifiers

Goal and Background

                This lab was twofold in the fact that it introduced and familiarized two advanced classification algorithms. The two classifications were linear spectral unmixing and fuzzy classifier. These algorithms are extremely helpful in increasing the accuracy of a classified image. The advanced classifiers use high-powered algorithms to achieve these high accuracies.

Methods

The first art of the lab was to use linear spectral unmixing. For this section another image processing software called ENVI was used. ENVI is used so that pure pixels or endmembers can be viewed. To start the image that was used is ec_cpw2000.img. In ENVI there is an Available Bands List. RGB Color is selected instead of Gray Scale. Here is where the layers of the image are put in. This is similar to layer stacking in ERDAS Imagine. Layer 4 was put into the red color gun, layer 3 into the green, and layer 2 into the blue. This created a False Color image that will be the image used for endmember selection. Next was to create a principal component image. This is six different images for each reflective band. The image is ec_cpw2000pc. PC Band 1 and 2 contain information for all the bands. The image, PC Band 1, along with the ETM+ image are loaded into ENVI. Next the scatterplot is made. To create the scatterplot there needs to be ab X and Y axis. PC Band 1 is X and PC Band 2 is Y. The scatterplot is created and is triangular in shape. To collect endmembers a circle is drawn around one of the vertices. The first endmembers that are collected are bare soil. To create these circles the Class tab was selected, then Items 1:20, and finally Green. A green circle was drawn around one of the outer vertices. The pixels on the ETM+ image that corresponded to this vertex are highlighted green. The same procedure is followed to find water and agriculture endmembers. Water endmembers are set to blue and agriculture is set to yellow. These endmembers need to be saved in the form of an Export All. This opens the ROI Tool window. The ROI file should be saved. The procedure to make the scatterplot is carried out again to find urban areas. Instead of PC 1 and 2, PC 3 and 4 are selected for X and Y. Exporting this class as an ROI is done. Finally, linear spectral unmixing can be done. This is done by Spectral > Mapping Methods > Linear Spectral Unmixing. Ec_cpw2000.img is the input image. This will lead to importing the endmember classes. The output image for this lab is ec_cpw2000frac. This creates four separate images that highlight areas in white that are highly correlated to the endmember classes.

The second part of this lab is fuzzy classification. Fuzzy classification is an advanced classifier that is set out to help increase accuracy. The process of carrying out this classification is very similar to a supervised classification. Like a supervised classification numerous training samples are taken. All the training samples of a class are merged like in Lab 5. The signature files are saved. To perform a fuzzy classification the supervised classification window was selected, the image ec_cpw2000.img is input, the saved training sample signatures is the signature file, an output is created. Now here is where the change occurs selecting of the fuzzy classification button and distance file button, maximum likelihood is the parametric rule, non-parametric rule is feature space, and 5 classes for the best classes per pixel.

Results

Fuzzy Classification LULC Map