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A Comparison of the Performance of Non-Parametric Classifiers with Gaussian Maximum Likelihood for the Classification of Multispectral Remotely Sensed Data.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Nessmiller, Steven W. |
| Copyright Year | 1995 |
| Abstract | Abstract : This study compares the performance of two non-parametric classifiers and Gaussian Maximum Likelihood (GML) for the classification of LANDSAT TM 30-meter resolution six-band data. The mathematical assumptions made in developing GML are valid if the pixels that constitute the training classes are normally distributed. Since it requires a model of the data, GML is termed a "parametric" classifier. Of current interest are new classification methodologies that make no assumptions about the statistical distribution of the pixels in the training class; these approaches are termed non-parametric' classifiers. This study will compare the n-Dimensional Probability Density Function (nPDF) essentially a projection technique that reduces data dimensionality, and an advanced neural network that utilizes fuzzy-set mathematics, the Fuzzy ARTMAP, to the traditional GML approach to image classification. The different approaches will be compared for statistical classification accuracy and computational efficiency. (AN) |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://scholarworks.rit.edu/cgi/viewcontent.cgi?article=3835&context=theses |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
