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At least 631 records · Page 35

Selecting class weights to minimize classification bias in acreage estimation

Preliminary results of experiments being performed to select optimal class weights for use with the maximum likelihood classifier in acreage estimation using remote sensor imagery are presented. These weights will be optimal in the sense that the bias will be minimized in the proportion estimate obtained from the classification results by sample counting. The procedure was tested using Landsat MSS data from an 8 by 9.6 km area of ground truth in Finney County, Kansas.

Belcher, W. M.↗

Evaluation of classification procedures for estimating wheat acreage in Kansas

This report presents the results of experiments which were performed to evaluate procedures for estimating wheat acreage in intensive test sites (ITS's) in Kansas. An analyst/interpreter (AI) selected and labeled fields from Landsat-1 satellite imagery. Statistics were generated for each selected ITS, and the imagery was classified using a maximum likelihood classifier. Various components of the classification process were tested.

Flores, L. M.↗

Flood-plain delineation using multispectral data analysis

The paper explores the application of a remote sensing technique that may permit the determination of flood-plain areas without the extensive work associated with existing techniques. Multispectral scanner data were simulated by utilizing the density differences in a color-infrared transparency for a section of the Navasota River, Texas. The transparency was taken from a low-flying aircraft and covered an area approximating a square mile. The simulated data were processed by an automatic classification technique previously developed in the remote sensing field. The technique used involves the application of the maximum likelihood rule in order to categorize the data being processed. An attempt was made to distinguish between areas known to be in the flood plain and those outside. A reasonabke correlation was found between boundaries based on computer-processed multispectral data and those produced by techniques currently in use.

Harker, G. R.↗

Identification of state variables and dynamic inflow from rotor model dynamic tests

The paper describes methods for extracting unknown state variables and parameters from dynamic rotor model tests given transient cyclic pitch stirring inputs, blade root flap-bending measurements, and the form of the dynamic rotor equations, including a rotor dynamic inflow description, when none of the physical parameters are known. A simplified version of the maximum likelihood method seems best suited for this purpose. The measurement equation error covariance matrix is assumed constant during each iteration, but updated for the subsequent iteration. A detailed analysis of the suitability of the derived techniques for studying various rotor dynamic inflow effects is provided.

Banerjee, D.↗

An empirical study of scanner system parameters

Parametric values (instantaneous field of view, number and location of spectral bands, signal-to-noise ratio, etc.) of a multispectral scanner (thematic mapper) are assigned empirically. An empirical simulation approach was used with candidate parameter values selected by heuristic means. Results obtained using a conventional maximum likelihood pixel classifier suggest that although the classification accuracy declines slightly as the instantaneous field of view (IFOV) is decreased this is more than made up by an improved mensuration accuracy. Use of a classifier involving both spatial and spectral features helps resist degradation as the signal-to-noise ratio is decreased. The importance of having at least one spectral band in each of the major available portions of the optical spectrum is demonstrated.

Landgrebe, D. A.↗

Simulated performance of coded PCM/FM modem on Rician fading channel

This paper describes a simulation approach to characterizing the performance of a coded PCM/FM modem operating on the classical Rician fading channel. In particular, we consider the performance of short constraint length convolutional codes in conjunction with Viterbi maximum likelihood decoding. The receiver consists of a narrowband filter followed by an ideal limiter/discriminator. Primary interest is in the bit error probability performance parameterized by both the fading channel and receiver parameters. Particular attention is given to simulating the impulse noise appearing at the discriminator output. This is the 'click' noise caused by encirclements of the origin by the instantaneous signal-plus-noise phasor. Results include the effects of channel memory and narrowband receiver filtering effects on overall bit error probability.

Modestino, J. W.↗

Flight data processing with the F-8 adaptive algorithm

An explicit adaptive control algorithm based on maximum likelihood estimation of parameters has been designed for NASA's DFBW F-8 aircraft. To avoid iterative calculations, the algorithm uses parallel channels of Kalman filters operating at fixed locations in parameter space. This algorithm has been implemented in NASA/DFRC's Remotely Augmented Vehicle (RAV) facility. Real-time sensor outputs (rate gyro, accelerometer and surface position) are telemetered to a ground computer which sends new gain values to an on-board system. Ground test data and flight records were used to establish design values of noise statistics and to verify the ground-based adaptive software. The software and its performance evaluation based on flight data are described

Hartmann, G.↗

On the error probability of general trellis codes with applications to sequential decoding

An upper bound on the average probability of error for maximum-likelihood decoding of the ensemble of random L-branch binary trellis codes of rate R = 1/n with distinction between memory length and tail length is given. It is shown that the bound is independent of the length L of the information sequence if the memory length exceeds the tail length by a specified amount that depends on L. Sequential decoding simulations using the stack algorithm were conducted to test the dependence of the undetected error probability on tail length and memory length, and the results corroborated the theory.

Johannesson, R.↗

The effect of the atmosphere on the classification of satellite observations to identify surface features

The effect of differences in atmospheric turbidity on the classification of Landsat 1 observations of a rural scene is presented. The observations are classified by an unsupervised clustering technique. These clusters serve as a training set for use of a maximum-likelihood algorithm. The measured radiances in each of the four spectral bands are then changed by amounts measured by Landsat 1. These changes can be associated with a decrease in atmospheric turbidity by a factor of 1.3. The classification of 22% of the pixels changes as a result of the modification. The modified observations are then reclassified as an independent set. Only 3% of the pixels have a different classification than the unmodified set. Hence, if classification errors of rural areas are not to exceed 15%, a new training set has to be developed whenever the difference in turbidity between the training and test sets reaches unity.

Fraser, R. S.↗

Performance of convolutionally encoded noncoherent MFSK modem in fading channels

The performance of a convolutionally encoded noncoherent multiple-frequency shift-keyed (MFSK) modem utilizing Viterbi maximum-likelihood decoding and operating on a fading channel is described. Both the lognormal and classical Rician fading channels are considered for both slow and time-varying channel conditions. Primary interest is in the resulting bit error rate as a function of the ratio between the energy per transmitted information bit and noise spectral density, parameterized by both the fading channel and code parameters. Fairly general upper bounds on bit error probability are provided and compared with simulation results in the two extremes of zero and infinite channel memory. The efficacy of simple block interleaving in combatting channel memory effects are thoroughly explored. Both quantized and unquantized receiver outputs are considered.

Modestino, J. W.↗

Effects of control inputs on the estimation of stability and control parameters of a light airplane

The maximum likelihood parameter estimation technique was used to determine the values of stability and control derivatives from flight test data for a low-wing, single-engine, light airplane. Several input forms were used during the tests to investigate the consistency of parameter estimates as it relates to inputs. These consistencies were compared by using the ensemble variance and estimated Cramer-Rao lower bound. In addition, the relationship between inputs and parameter correlations was investigated. Results from the stabilator inputs are inconclusive but the sequence of rudder input followed by aileron input or aileron followed by rudder gave more consistent estimates than did rudder or ailerons individually. Also, square-wave inputs appeared to provide slightly improved consistency in the parameter estimates when compared to sine-wave inputs.

Cannaday, R. L.↗

Computer-aided classification for remote sensing in agriculture and forestry in Northern Italy

A set of results concerning the processing and analysis of data from LANDSAT satellite and airborne scanner is presented. The possibility of performing inventories of irrigated crops-rice, planted groves-poplars, and natural forests in the mountians-beeches and chestnuts, is investigated in the Po valley and in an alphine site of Northern Italy. Accuracies around 95% or better, 70% and 60% respectively are achieved by using LANDSAT data and supervised classification. Discrimination of rice varieties is proved with 8 channels data from airborne scanner, processed after correction of the atmospheric effect due to the scanning angle, with and without linear feature selection of the data. The accuracies achieved range from 65% to more than 80%. The best results are obtained with the maximum likelihood classifier for normal parameters but rather close results are derived by using a modified version of the weighted euclidian distance between points, with consequent decrease in computing time around a factor 3.

Dejace, J.↗

Performance tests of signature extension algorithms

Comparative tests were performed on seven signature extension algorithms to evaluate their effectiveness in correcting for changes in atmospheric haze and sun angle in a LANDSAT scene. Four of the algorithms were cluster matching, and two were maximum likelihood algorithms. The seventh algorithm determined the haze level in both training and recognition segments and used a set of tables calculated from an atmospheric model to determine the affine transformation that corrects the training signatures for changes in sun angle and haze level. Three of the algorithms were tested on a simulated data set, and all of the algorithms were tested on consecutive-day data.

Abotteen, R. A.↗

Flight-determined stability and control coefficients of the F-111A airplane

A complete set of linear stability and control derivatives of the F-111A airplane was determined with a modified maximum likelihood estimator. The derivatives were determined at wing sweep angles of 26 deg, 35 deg, and 58 deg. The flight conditions included a Mach number range of 0.63 to 1.43 and an angle of attack range of 2 deg to 15 deg. Maneuvers were performed at normal accelerations from 0.9g to 3.8g during steady turns to assess the aeroelastic effects on the stability and control characteristics. The derivatives generally showed consistent trends and reasonable agreement with the wind tunnel estimates. Significant Mach effects were observed for Mach numbers as low as 0.82. No large effects attributable to aeroelasticity were noted.

Iliff, K. W.↗

Quasi-Newton Methods

The problem to be solved is formulated precisely and the introduction of quasi-Newton methods is motivated by considering the classical Newton and secant methods and their properties. Three highly successful quasi-Newton methods are surveyed: Broyden's method for the solution of general nonlinear equations, and the Davidon-Fletcher-Powell and Broyden-Fletcher-Goldfarb-Shanno procedures for unconstrained minimization. Finally, the properties of these methods are compared to those of Newton's method and UHMLE in potential applications to maximum-likelihood estimation of parameters in mixture distributions.

Walker, H. F.↗

Estimation of the sea surface's two-scale backscatter parameters

The relationship between the sea-surface normalized radar cross section and the friction velocity vector is determined using a parametric two-scale scattering model. The model parameters are found from a nonlinear maximum likelihood estimation. The estimation is based on aircraft scatterometer measurements and the sea-surface anemometer measurements collected during the JONSWAP '75 experiment. The estimates of the ten model parameters converge to realistic values that are in good agreement with the available oceanographic data. The rms discrepancy between the model and the cross section measurements is 0.7 db, which is the rms sum of a 0.3 db average measurement error and a 0.6 db modeling error.

Wentz, F. J.↗

Estimation from incomplete multinomial data

The vector of multinomial cell probabilities was estimated from incomplete data, incomplete in that it contains partially classified observations. Each such partially classified observation was observed to fall in one of two or more selected categories but was not classified further into a single category. The data were assumed to be incomplete at random. The estimation criterion was minimization of risk for quadratic loss. The estimators were the classical maximum likelihood estimate, the Bayesian posterior mode, and the posterior mean. An approximation was developed for the posterior mean. The Dirichlet, the conjugate prior for the multinomial distribution, was assumed for the prior distribution.

Credeur, K. R.↗

Signal-to-noise ratio estimation in digital computer simulation of lowpass and bandpass systems with applications to analog and digital communications, volume 3

Techniques are developed to estimate power gain, delay, signal-to-noise ratio, and mean square error in digital computer simulations of lowpass and bandpass systems. The techniques are applied to analog and digital communications. The signal-to-noise ratio estimates are shown to be maximum likelihood estimates in additive white Gaussian noise. The methods are seen to be especially useful for digital communication systems where the mapping from the signal-to-noise ratio to the error probability can be obtained. Simulation results show the techniques developed to be accurate and quite versatile in evaluating the performance of many systems through digital computer simulation.

Tranter, W. H.↗