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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Infrared studies
Infrared scanner use for thermal mapping and multispectral sensing in Remote Sensing Aircraft Program
Infrared studies
Investigating infrared scanners for thermal mapping and multispectral sensing
Imaging radars for geoscience use
Side-looking airborne radars and image recording scanners design for geoscience applications, discussing gray scale improvement, multispectral sensing, target discrimination, etc
Detailed interpretation and analysis of selected corn blight watch data sets
A detailed interpretation and analysis of selected corn blight data set was undertaken in order to better define the present capabilities and limitations of agricultural remote multispectral sensing and automatic processing techniques and to establish the areas of investigation needing futher attention in the development of operational survey systems. While the emphasis of this effort was directed toward the detection of various corn blight levels, problems related to the more general task of crop identification were also investigated. Since the analog recognition computer (SPARC) was fully committed to the more routine aspects of processing and since the detailed interpretation and analysis required more in the way of quantitative information, the CDC 1604 digital computer was employed.
ERTS computer compatible tape data processing and analysis. Appendix 1: The utility of imaging radars for the study of lake ice
There are no author-identified significant results in this report. Remotely sensed multispectral scanner and return beam vidicon imagery from ERTS-1 is being used for: (1) water depth measurements in the Virgin Islands and Upper Lake Michigan areas; (2) mapping of the Yellowstone National Park; (3) assessment of atmospheric effects in Colorado; (4) lake ice surveillance in Canada and Great Lakes areas; (5) recreational land use in Southeast Michigan; (6) International Field Year on the Great Lakes investigations of Lake Ontario; (7) image enhancement of multispectral scanner data using existing techniques; (8) water quality monitoring of the New York Bight, Tampa Bay, Lake Michigan, Santa Barbara Channel, and Lake Erie; (9) oil pollution detection in the Chesapeake Bay, Gulf of Mexico southwest of New Orleans, and Santa Barbara Channel; and (10) mapping iron compounds in the Wind River Mountains.
Use of data from space for earth resources exploration and management in Alabama
The University of Alabama, the Geological Survey of Alabama, and the George C. Marshall Space Flight Center are involved in an interagency, interdisciplinary effort to use remotely sensed, multispectral observations to yield improved and timely assessment of earth resources and environmental quality in Alabama. It is the goal of this effort to interpret these data and provide them in a format which is meaningful to and readily usable by agencies, industries, and individuals who are potential users throughout the State.
Marine resources and ocean surveys
Application of ERTS-1 remotely sensed multispectral imagery to marine resources and oceanic observations and surveys - Conference
Use of Skylab EREP data in a sea surface temperature experiment
The author has identified the following significant results. This report discusses an experiment to be performed on each of the three manned Skylab missions, the results of which will assess the ability of spaceborne infrared multispectral sensing to function as a means of providing improved estimates of sea surface temperature over that obtainable with a single channel radiometric instrument. The overall investigative program is outlined, the effort performed during the prelaunch phase is detailed, and pertinent results are presented.
ASTEP user's guide and software documentation
The Algorithm Simulation Test and Evaluation Program (ASTEP) is a modular computer program developed for the purpose of testing and evaluating methods of processing remotely sensed multispectral scanner earth resources data. ASTEP is written in FORTRAND V on the UNIVAC 1110 under the EXEC 8 operating system and may be operated in either a batch or interactive mode. The program currently contains over one hundred subroutines consisting of data classification and display algorithms, statistical analysis algorithms, utility support routines, and feature selection capability. The current program can accept data in LARSC1, LARSC2, ERTS, and Universal formats, and can output processed image or data tapes in Universal format.
Machine boundary finding and sample classification of remotely sensed agricultural data
A method based on the use of spectral variations in combination with spatial variations is developed for automatic boundary finding and sample classification of remotely sensed multispectral data. Preliminary applications of the method to agricultural data show significant improvements in accuracy as compared to the use of spectral data alone.
Cluster analysis based on dimensional information with applications to feature selection and classification
A new clustering algorithm is presented that is based on dimensional information. The algorithm includes an inherent feature selection criterion, which is discussed. Further, a heuristic method for choosing the proper number of intervals for a frequency distribution histogram, a feature necessary for the algorithm, is presented. The algorithm, although usable as a stand-alone clustering technique, is then utilized as a global approximator. Local clustering techniques and configuration of a global-local scheme are discussed, and finally the complete global-local and feature selector configuration is shown in application to a real-time adaptive classification scheme for the analysis of remote sensed multispectral scanner data.
Classification software technique assessment
A catalog of software options is presented for the use of local user communities to obtain software for analyzing remotely sensed multispectral imagery. The resources required to utilize a particular software program are described. Descriptions of how a particular program analyzes data and the performance of that program for an application and data set provided by the user are shown. An effort is made to establish a statistical performance base for various software programs with regard to different data sets and analysis applications, to determine the status of the state-of-the-art.
Data resolution versus forestry classification and modeling
This paper examines the effects on timber stand computer classification accuracies caused by changes in the resolution of remotely sensed multispectral data. This investigation is valuable, especially for determining optimal sensor and platform designs. Theoretical justification and experimental verification support the finding that classification accuracies for low resolution data could be better than the accuracies for data with higher resolution. The increase in accuracy is constructed as due to the reduction of scene inhomogeneity at lower resolution. The computer classification scheme was a maximum likelihood classifier.
The LARSYS Educational Package: Instructor's Notes for Use with the Data 100
The LARSYS Educational Package is a set of instructional materials developed to train people to analyze remotely sensed multispectral data using LARSYS, a computer software system. The materials included in this volume have been designed to assist LARSYS instructors as they guide students through the LARSYS Educational Package. All of the materials have been updated from the previous version to reflect the use of a Data 100 Remote Terminal.
Maximum likelihood estimation of label imperfections and its use in the identification of mislabeled patterns
The problem of estimating label imperfections and the use of the estimation in identifying mislabeled patterns is presented. Expressions for the maximum likelihood estimates of classification errors and a priori probabilities are derived from the classification of a set of labeled patterns. Expressions also are given for the asymptotic variances of probability of correct classification and proportions. Simple models are developed for imperfections in the labels and for classification errors and are used in the formulation of a maximum likelihood estimation scheme. Schemes are presented for the identification of mislabeled patterns in terms of threshold on the discriminant functions for both two-class and multiclass cases. Expressions are derived for the probability that the imperfect label identification scheme will result in a wrong decision and are used in computing thresholds. The results of practical applications of these techniques in the processing of remotely sensed multispectral data are presented.
Design, implementation, and results of LACIE field research
The capability to acquire, process, and interpret remotely sensed multispectral measurements of the energy reflected and emitted from crops, soils, and other Earth surface features is considered. The LACIE Field Measurements Project is described including project objectives, the experimental approach, the data acquisition program, and selected results based on field data. The key accomplishments and results of the experiment and recommendations for future field research are summarized.
Supplemental US/Canada wheat and barley exploratory experiment implementation plan: Evaluation of a procedure 1A technology
A plan is presented for a supplemental experiment to evaluate a sample allocation technique for selecting picture elements from remotely sensed multispectral imagery for labeling in connection with a new crop proportion estimation technique. The method of evaluating an improved allocation and proportion estimation technique is also provided.