Lab 8: Advanced Classifiers I

Background and Goal

The goal of this lab is to learn two advanced classification algorithms. These advanced classifiers are much more robust, and produce a classified image that is more accurate than those produced by unsupervised and supervised classifiers that were used in previous labs. The two algorithms that are learned in this lab are Spectral Linear Unmixing and Fuzzy classification. Spectral Linear Unmixing utilizes the measurement of ‘pure’ pixels (also known as endmembers) to classify images. Fuzzy classification accounts for mixed pixels in an image. Some pixels are a combination of several classes (due to spectral resolution) and thus membership grades are used to determine which class is more strongly identified within the pixel.

Methods

To conduct a spectral linear unmixing classification, it was first necessary to transform the image into a Principal Components (PC) image. This technique compiles all information into more compact bands, providing most of the image’s information within the first two to three bands in the PC image. This function was done with ENVI. For our image, most of the information was compiled in bands 1 and 2 of the PC image, with some information in band 3. To collect endmembers from the PC image, we created scatterplots of the informational bands. First, a scatterplot was generated with band 1 and the x-axis and band 2 and the y-axis. This created a scatterplot with a triangular shape. The points of this triangle are the ‘pure’ pixels (endmembers). These points were selected and turned into a class. The first scatter plot included the endmembers for water, agriculture, and bare soil (Figure 1 shows this scatterplot). However, to complete the classification, we also need endmembers for the urban class. We created a second scatterplot of bands 3 and 4 to collect these endemembers (Figure 1 shows this scatterplot as well). The endmembers are also highlighted on the reference image to make sure that the correct endmembers were selected (Figure 2). All of these endmembers were then exported into a region of interest (ROI) file and were then used in the Linear Spectral Unmixing function in ENVI, producing fractional maps for each endmember.

Figure 1: The scatterplots used to collect endmembers. The colors correspond to the colors in the reference map in Figure 2.

Figure 2: The reference map for collecting endmembers. The areas highlighted with green are the bare soil endmembers, yellow are the agriculture endmembers, blue are the water endmembers, and purple are the urban endmembers.

The processing for fuzzy classification was all executed within ERDAS. The first step is to select signatures from the input image. These signatures must be from areas where there is a mixture of land cover classes, as well as homogenous land cover. The aoi’s for the signature samples had to contain between 150 and 300 pixels. Four samples were collected for the water, forest, and bare soil classes, and six samples were collected for the agriculture and urban/built up classes. The samples for each of the classes were then merged, to create one aggregated sample for each class. These signatures were then used to create a fuzzy classification with ERDAS’ supervised classification function. This first function creates five layers of classified images, ranking the most probable classes for each pixel. A fuzzy convolution algorithm was then used in ERDAS to turn these layers into one classified image.

Results

Figures 3 through 6 show the results of the linear spectral unmixing function. The bare soil fractional map is quite accurate. It highlights the bare area surrounding the airport very well, as well as the empty crop fields to the east and the west. The fractional map for agriculture is a little less accurate. It seems to slightly highlight vegetation in general, and not just agriculture areas. The water fractional map is surprisingly inconsistent. Usually water is easy to classify, and it was classified well. However, the map also classified areas that were not water (notably the area around the airport). The urban fractional map worked surprisingly well. It successfully highlights the urban areas, but fails to leave other areas in the dark.

Figure 3: The Bare Soil fractional map.

Figure 4: The Agriculture fractional map.

Figure 5: The Water fractional map.

Figure 6: The Urban fractional map.

Figure 7 shows the final result of the fuzzy classification. This classification worked much better than the supervised classification in the previous lab, though the urban and agriculture classes were still over predicted.

Figure 7: The final result of the fuzzy classification.

Sources

Earth Resources Observation and Science Center. (2000). [Landsat image of the Eau Claire and Chippewa Counties]. United States Geological Survey. Provided by Cyril Wilson.