Applying the error analysis platform in improving the accuracy of vegetation image classificationTake the campus of Taiwan University as an example
Date Issued
2008
Date
2008
Author(s)
Liou, Wei-Ting
Abstract
While the accuracy for interpretation of satellite images in land-use surveys is required to exceed 95% in some cases, automatic classification of such images can only reach an average accuracy level of about 85%. Although artificial interpretation can secure perfect accuracy of 100%, it is too labor-consuming to be practical. This thesis focuses on how to increase the accuracy for using auto-classification to a required level. Using a maximum likelihood method, satellite images are automatically classified into several types of land cover, each with a computer-determined probability. For the probability for a type of land cover, the lower or the closer to the probability for another type of land cover, the more likely an error in automatic interpretation. This is an error analysis platform used in the thesis. For an image, three types of land cover with the top three probabilities are selected for error analysis. For a type of land cover, if its probability is lower than a specified critical value or the difference between the probability and another probability (for another type of land cover) is lower than another specified critical value (the two probabilities are too close), artificial interpretation through overlapping picture elements on homogeneous areas is made to find whether the auto-classification is correct. Using the error analysis, it is found that too low probabilities for types of land cover or too close probabilities between two types of land cover are the main reason behind the interpretation error due to auto-classification. However, shades in images or variation in vegetation also account for such errors and the two factors are not suitable for being incorporated into the error analysis.
Subjects
accuracy
Maximum Likelihood classifier
error analysis
pixel probability
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-97-R93228014-1.pdf
Size
23.53 KB
Format
Adobe PDF
Checksum
(MD5):ef0afa28cb712037124ce00fb5e26762