Advanced Classifiers 3: Expert System and Advanced Neural Network

Introduction:

The advanced classifiers used in the previous labs were each robust, however, they aren't the only methods to achieve high classification accuracy. Artificial Neural Network (ANN) is a classification method that simulates the thinking process of the human brain to implement machine learning, allowing the classifier to perform parameter adjustments independent of a remote sensing expert. Expert System (ES) uses a decision tree to refine an existing classified image using ancillary data. When used correctly, ES can yield incredibly accurate results (93% Overall Accuracy reported in one study).

Methods:

The first method used in this exercise was ES. The ES was used to refine the classification of an image, using several ancillary images containing boolean values for specific classes. The decision tree was designed to refine the classification of several classes (urban/built-up, agriculture, and green vegetation), including dividing urban/built-up into residential and non-residential 'urban' areas. The non-residential urban areas were identified in one of the ancillary boolean rasters, and a rule was created so the output image only contained the intersecting areas between the urban/built-up class of the input raster and the non-residential areas ancillary raster. The residential areas were identified by selecting only the areas classified as urban/built-up on the input raster that were not overlapped by values of the non-residential areas raster. The ancillary rasters for green vegetation and agriculture were used to refine their respective classes in a similar manner to the one used to refine the urban/built-up classes.

ANN was implemented on Quickbird imagery of Northern Iowa University. Once samples for the desired classes are collected, they are fed into the classifier. The classifier was set to use 1000 training iterations when classifying the image.

Results/Conclusions:

The Expert System classification greatly increased the accuracy of the previous image(Figure 1). It allowed for more differentiation of classes, and fixed several problem areas (Figure 2). The ANN classification was extremely easy to implement, but didn't explain any of its parameters, so it isn't possible to know exactly what parameters it used to perform its final classification.

Figure 1: The Expert System Classified image.

Figure 2: The commercial area in the center/right was reclassified to 'Other Urban',
while the surrounding areas were reclassified to 'Residential'