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Metric camera photographs are used to analyze volcanic and surficial features of the lunar surface between Mare Smythii and King Crater. Patterns created by lava flows, ejecta blankets, impact debris, and fissures are discussed in detail. Possible causes of each phenomena are suggested.
Primary efforts were made to outline areas where low-relief features in the Apollo 16 landing site and associated plains and mare units were intensified in near-terminator photography. A description and, in some cases, a preliminary interpretation of several of these areas is included in an attempt to provide additional data relevant to the interpretation of the hills, plains, and associated units investigated on the Apollo 16 mission. Near-terminator photography for areas discussed is listed in a table.
Analysis of the ERTS data in portions of the eastern Caribbean are presented for October 1972 showing features which are, as yet, not explained. Ground truth data obtained in that area during November 1972 are presented. These include vertical temperature structure in the mixed layer and thermocline, and surface measurements of salinity, temperature, and chlorophyll.
The problem dealt with concerns feature selection or reducing the dimension of the data to be processed from n to k. By reducing the dimension of the data from n to k, classification time is generally reduced. Yet the dimension reduction should not be so great that classification accuracy is impaired. Thus, the general problem is considered of classifying an n-dimensional observation vector x into one of m-distinct classes where each class is normally distributed with mean and covariance. It is shown that the probability of misclassification is minimized if a maximum likelihood classification procedure is used to classify the data. The dimension of each observation vector to be processed is conveniently reduced by performing the transformation y = Bx, where B is a K by n matrix of rank k. Thus, the n-dimensional classification problem transforms into a k-dimensional classification problem.
Systematic Mariner 9 photography of a range of Martian surface features, observed with all three photometric angles approximately invariant, reveals three general categories of albedo variations: (1) an essentially uniform contrast enhancement due to the dissipation of the dust storm; (2) the appearance of splotches, irregular dark markings at least partially related to topography; and (3) the development of both bright and dark linear streaks, generally emanating from craters. Some splotches and streaks vary on characteristic timescales of about two weeks; they have characteristic dimensions of kilometers to tens of kilometers. The morphology and variability of streaks and splotches, and the resolution of at least one splotch into an extensive dune system, implicate windblown dust as the principal agent of Martian albedo differences and variability.
Leventhal noticed that the gamma-ray spectrum due to the annihilation of positronium, which consists of two 511 keV photons from the singlet state and three photons from the triplet state, produces a spectral feature with an apparent peak at an energy less than 511 keV when viewed with a gamma-ray telescope having a Gaussian energy resolution. He calculated that the observed peak will lie at 490 keV. The author calculates that if the positronium spectrum sits atop a steeply falling continuum due to other sources, then the apparent peak can easily fall near 476 keV where it was observed. It is shown that explosive nucleosynthesis is a plausible source of the positrons.
Martian albedo features photographed from the earth are compared with the topography observed by Mariner 9. Three Lambert azimuthal equal-area charts prepared to facilitate the comparison are presented. No correlation between large-scale albedos and topography can be generalized, but several different kinds of relationships seem to be indicated in particular regions. Examples of these are discussed.
The author has identified the following significant results. The land use category of subimage regions over Kansas within an MSS image can be identified with an accuracy of about 70% using the textural-spectral features of the multi-images from the four MSS bands.
Feature selection software was developed at the Earth Resources Laboratory that is capable of inputting up to 36 channels and selecting channel subsets according to several criteria based on divergence. One of the criterion used is compatible with the table look-up classifier requirements. The software indicates which channel subset best separates (based on average divergence) each class from all other classes. The software employs an exhaustive search technique, and computer time is not prohibitive. A typical task to select the best 4 of 22 channels for 12 classes takes 9 minutes on a Univac 1108 computer.
A method of linear feature selection for n dimensional observation vectors which belong to one of m populations is presented. Each population has a known apriori probability and is described by a known multivariate normal density function. Specifically we consider the problem of finding a k x n matrix B of rank k (k n) for which the transformed probability of misclassification is minimized. Providing that the transformed a posterior probabilities are distinct theoretical results are obtained which, for the case k = l, give rise to a numerically tractable formula for the derivative of the probability of misclassification. It is shown that for the two population problem this condition is also necessary. The dependence of the minimum probability of error on the a priori probabilities is investigated. The minimum probability of error satisfies a uniform Lipschitz condition with respect to the a priori probabilities.
A condition for the Gateaux differentiability of the probability of misclassification as a function of a feature selection matrix B, assuming a maximum likelihood classifier and normally distributed populations, is given. It is also shown that if the probability of error has a local minimum at B then it is differentiable at B.
Variational equations are presented for maximizing the probability of correct classification as a function of a 1xn feature selection matrix B for the two-population problem. For the special case of equal covariance matrices the optimal B is unique up to scalar multiples and rank one sufficient. For equal population means, the best 1xn B is an eigenvector corresponding either to the largest or smallest eigenvalue of sigma sub 2 to the minus 1 power sigma sub 1 where sigma sub 1 and sigma sub 2 are the nxn covariance matrices of the two populations. The transformed probability of correct classification depends only on the eigenvalue. Finally, a procedure is proposed for constructing an optimal or nearly optimal kxn matrix of rank k without solving the k-dimensional variational equation.
The Space Shuttle Main Engine (SSME) is a reusable, high-performance rocket engine being developed to satisfy the performance, life, reliability, and operational requirements of the Space Shuttle Orbiter. The design includes simple, low-cost maintenance features resulting from a viable maintainability program dedicated to minimizing engine cost per flight.
The B-average divergence for m-distinct classes, resulting from the linear transformation y = Bx, is proposed as a feature selection criterion, where B is a k by n matrix of rank k not greater than n. It is shown that if the B-average divergence resulting from B is large enough, then the probability of misclassification, considered as a function f the class of all k by n matrices, is essentially minimized by B. A computer program, utilizing a gradient procedure, is developed to numerically maximize the B-average divergence and results are presented for the Cl flight line. For this example, corresponding to 9-distinct classes, most of the discriminatory information is found to lie in a 3-dimensional subspace, defined by an appropriately chosen 3 by 12 matrix B.
The use of a Monte Carlo model for generating sample directional reflectance data for two simplified target canopies at two different solar positions is reported. Successive iterations through the model permit the calculation of a mean vector and covariance matrix for canopy reflectance for varied sensor view angles. These data may then be used to calculate the divergence between the target distributions for various wavelength combinations and for these view angles. Results of a feature selection analysis indicate that different sets of wavelengths are optimum for target discrimination depending on sensor view angle and that the targets may be more easily discriminated for some scan angles than others. The time-varying behavior of these results is also pointed out.
Mariner 9 (M9) and Mariner 6 and 7 photography of common regions of Mars are compared, with appropriate attention to the photometric properties of the camera systems. The comparison provides a 2.5 yr time baseline for study of variable albedo features. We find the development of bright streaks and patches, a phenomenon unobserved through the entire M9 mission; the evolution of dark crater splotches into dark streaks; and a planetwide increase in splotchiness. Yet, a large number of splotches and albedo boundaries remain fixed over the same period. Many of the observations are interpreted in terms of a global fallout and subsequent local redistribution of bright fine particulates raised by global dust storms.
Description of the salient characteristics of temporal variations of greater than or about equal to 100 keV/nucleon protons and alpha particles during a ten-hour period associated with the passage of an interplanetary shock wave. In addition to extending measurements to energies considerably below previous observations for energetic storm particle and shock-spike events, the discovery of a new low energy feature, referred to as 'postshock spikes', is reported. The typical width of these spikes is several minutes, but they occur several hours after the passage of the shock front. Evidence is presented that spikes depends on the energy per charge of the particles.
Techniques for solving the feature selection problem are presented. Topics discussed include the reduction of the number of variables in "best b", and the iterative selection of H sub i.