Engineering topics
Publications and source records attributed to Kan, E. P. F..
There are no author-identified significant results in this report.
The author has identified the following significant results. Results show that the new algorithm produced cleaner classification maps in which holes of small predesignated sizes were eliminated and significant boundary information was preserved. These cleaner post-processed maps better resemble true life timber stand maps and are thus more usable products than the pre-post-processing ones: Compared to an accepted neighbor-checking post-processing technique, the new algorithm is more appropriate for timber stand mapping.
There are no author-identified significant results in this report.
Three conclusions are drawn on the usability, inherent variations, and noise aspects of the spectral signatures processed from data collected by the Field Signature Acquisition System (FSAS). Conclusions are based on the spectral data collected from winter wheat of the 1972/73 season, grown at Texas A and M University, College Station, Texas.
The limitation of the iterative clustering procedure proposed by Haralick and Dinstein is indicated with the help of two examples and is construed as the effect of the linear structure of the T transformation.
Formulas are derived for updating the mean vector and covariance matrix of a training class as new training fields are included and old training fields deleted from the class. These statistics of the class are expressed in terms of the already available statistics of the fields.
The author has identified the following significant results. The parameter AD (average distance) as used in the ISODATA program was critically examined. Thresholds of AD to decide on the splitting of clusters were obtained. For the univariate case, 0.84 was established as a sound choice, after examining several simple, as well as composite, distributions and also after investigating the probability of misclassification when points have to be reassigned to the newly identified clusters. For the multivariate case, the empirical threshold (N-0.16)/square root of N was extrapolated. A final criticism on AD was that AD would lose its effectiveness as a discriminative measure for the present purpose when N was large.