A fuzzy grade-of-membership (GoM) clustering algorithm is applied to analysis of remote sensing data, in particular, the type of data used in climatic classification. The methodology is applied to a cloud product data subset derived from NASA’s International Satellite Cloud Climatology Project, which includes remotely sensed global monthly average surface temperature and precipitation data for land and coastal regions for the year 1984. GoM partitions for this case are similar to those of vector quantization and fuzzy c-means clustering algorithms, which is significant given the striking differences between the algorithms. The GoM clustering approach is shown to provide an alternative means of interpreting large heterogeneous datasets for exploratory analysis, which broadens the application base by admitting categorical data.
We show that when mean -square error is used to determine the performance of image compression algorithms, in particular vector quantization algorithms, the mean -square error measurement is dependent upon the data type of the digitized images. When using vector quantization the possibility exists for encoding images of one type with code books of another type, we show that this cross -encoding has an adverse effect on performance. Thus, when making comparative evaluations of different vector quantization compression techniques one must be careful to document the data type used in both the code book and the test image data. We also show that when mean -square error measurements are made in the perceptual space of a human visual model, the distortion measurements correlate more with subjective image evaluation than when the distortions are calculated in other spaces. We use a monochrome visual model to improve the quality of vector quantized images, but our preliminary results indicate that in general, the performance of the model is dependent upon the type of data and the coding method used.
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