Engineering topics

Mobasseri, B. G.

Publications and source records attributed to Mobasseri, B. G..

4 records

NASA NTRS1979

Multiclass Bayes error estimation by a feature space sampling technique

A general Gaussian M-class N-feature classification problem is defined. An algorithm is developed that requires the class statistics as its only input and computes the minimum probability of error through use of a combined analytical and numerical integration over a sequence simplifying transformations of the feature space. The results are compared with those obtained by conventional techniques applied to a 2-class 4-feature discrimination problem with results previously reported and 4-class 4-feature multispectral scanner Landsat data classified by training and testing of the available data.

Mobasseri, B. G.↗

NASA NTRS1979

A parametric model for multispectral scanners

A set of test statistics are specified and the corresponding output quantities computed by the characteristic function. Two sets of classification accuracies, one at the input and one at the output are estimated. The scanner's instantaneous field of view is changed and the variation of the output classification performance is monitored.

Mobasseri, B. G.↗

NASA NTRS1978

Multispectral scanner system parameter study and analysis software system description, volume 2

The author has identified the following significant results. The integration of the available methods provided the analyst with the unified scanner analysis package (USAP), the flexibility and versatility of which was superior to many previous integrated techniques. The USAP consisted of three main subsystems; (1) a spatial path, (2) a spectral path, and (3) a set of analytic classification accuracy estimators which evaluated the system performance. The spatial path consisted of satellite and/or aircraft data, data correlation analyzer, scanner IFOV, and random noise model. The output of the spatial path was fed into the analytic classification and accuracy predictor. The spectral path consisted of laboratory and/or field spectral data, EXOSYS data retrieval, optimum spectral function calculation, data transformation, and statistics calculation. The output of the spectral path was fended into the stratified posterior performance estimator.

Landgrebe, D. A.↗

NASA NTRS1978

A parametric multiclass Bayes error estimator for the multispectral scanner spatial model performance evaluation

The author has identified the following significant results. The probability of correct classification of various populations in data was defined as the primary performance index. The multispectral data being of multiclass nature as well, required a Bayes error estimation procedure that was dependent on a set of class statistics alone. The classification error was expressed in terms of an N dimensional integral, where N was the dimensionality of the feature space. The multispectral scanner spatial model was represented by a linear shift, invariant multiple, port system where the N spectral bands comprised the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial, and hence, spectral correlation matrices through the systems, was developed.

Mobasseri, B. G.↗