In the previous lab object based classification was used to get a higher classification accuracy than what was produced from the supervised and unsupervised classification techniques that were used earlier in the semester. This lab will go over expert classifiers that are used to enhance the results of the previous classifications.
Methods:
To use an expert system classifier, the user will need to have a classified image they wish to classify more accurately. To make a classified image more accurate the user can apply ancillary data to the image. This can be done using the Knowledge Engineer tool in ERDAS Imagine.
The rules that are being set in the image basically say that if the classification in the image that is already classified is not actually what the classification says it is, give it a value of 1. This will then change it to a different classification.
The next step is to use the Neural Network as an expert classifier to create an image with a more accurate classification. This will be done using the ENVI programs neural net tool. Within the tool the image is input and parameters are set.
Results:
This is the final image that was classified using the expert systems. The output of the classification looks really good. Most of the land cover areas are correctly classified on a qualitative level. To see how accurate this image actually is an accuracy assessment would need to be done.
Sources:
Cyril Wilson
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