Lab 9: Advanced Classifiers II

Background and Goal

In this lab, we learn about two more advanced classification algorithms. These algorithms produce classification maps that are much more accurate than those that are created with simple unsupervised and supervised classifiers. Specifically, this lab explores the use of an expert system classification (using ancillary data), and the development of an artificial neural network (ANN) classification. Both of these functions use very robust algorithms to create complex and highly accurate classification maps.

Methods

The use of expert systems is applied to increase the accuracy of previously classified images using ancillary data. In this lab, we used a classified image the Eau Claire, Altoona, and Chippewa Falls area (Figure 1). The map had the classes of Water, Forest, Agriculture, Urban/built up, and Green vegetation. In this classification map, Agriculture and Green Vegetation classes were over predicted. To use the expert system to correct these errors, it is first necessary to construct a knowledge base. Knowledge bases are built up of hypotheses, rules, and variables. Hypotheses are the targeted LULC classes, rules are the functions that will be used to classify the hypotheses, and variables are the inputs (previously classified image and ancillary data). For this lab, we created eight hypotheses. One was for water, one was for forest, one was for residential, one was for other urban, two were for green vegetation, and two were for agriculture. There are more hypotheses than classes because there must be a hypothesis for each correction. In the exercise, we broke the urban/built up class into ‘Residential’ and ‘Other Urban’, corrected green vegetation areas that were predicted as agriculture, and corrected agriculture areas that were predicted as green vegetation. The functions used bitwise language to classify the different hypotheses using the previously classified image and the ancillary data. Figure 2 shows the complete knowledge base. Once the knowledge base was complete, the classification was run producing a classification image with the eight hypotheses. These were then recoded into the six classes (basically just merging Green Vegetation with Green Vegetation 2 and merging Agriculture with Agriculture 2) complete the classification.

Figure 1: The original classified image of the Eau Claire, Altoona, and Chippewa Falls area.

Figure 2: The complete knowledge base. The hypotheses are on the left (green boxes), with corresponding rules on the right (blue boxes). There is a counter argument for each classifying argument.

ANN simulates the process of the human brain to perform image classification by ‘learning’ the patterns between remotely sensed images and ancillary data. It uses input nodes, hidden layers, and output nodes to bounce information back and forth and reveal the best answer based on the different Training Rates, Training Momentums, and Training Thresholds. In this lab, we used high resolution imagery of the University of North Iowa. To conduct the ANN classification, it was first necessary to create a training sample, by highlighting Regions of Interest (ROI). These ROI’s are essentially the classes that the image will be classified into. For this classification, the ROI’s were Rooftops, Pavement, and Grass. Figure 3 shows the reflective image with ROI’s highlighted. These ROI’s were then used as ancillary data in the ANN classification, with a Threshold of 0.95, a Training Rate of 0.18, and a Training Momentum of 0.7.

Figure 3: The image of UNI's campus overlayed with ROI's of grass (green), rooftop (red), and pavement (blue).

Results

The expert system classification produced a much more accurate image than the previous classified image. The image corrected the over prediction of agriculture and green vegetation, as well as creating two classes for the urban/built up class. Figure 4 shows the result of the expert system classification.

Figure 4: The result of the expert system classification method. It is much more accurate than the previous image.

The ANN classification was surprisingly easy to use, and produced an easily readable classification image. Figure 5 shows the results. It is easy to tell where the roads and the grass are. However, it classified the trees as rooftops (due to their shadows), and some of the rooftops were classified as pavement. Though the image isn't terribly accurate, it is surprisingly easy to distinguish between features, considering the work that the analyst has to do.

Figure 5: The classification image of the ANN classification method. Green areas are grass, blue areas are pavement, and red areas are rooftops.

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.

Department of Geography. (2003). [Quickbird High Resolution image of the University of Northern Iowa campus]. University of Northern Iowa. Provided by Cyril Wilson.