Engineering topics

Wheeler, S. G.

Publications and source records attributed to Wheeler, S. G..

6 records

NASA NTRS1979

An evaluation of procedure 1

LACIE Procedure 1 has undergone continuous testing and evaluation, starting with analytical and experimental studies even before it was implemented in ERIPS software and continuing to the present with performance evaluations using blind-site data. The strengths and weaknesses of the procedure are indicated and some areas for possible improvement are identified. Results from three of the experiments performed and an evaluation of LACIE Procedure 1 proportion estimates for some blind-site segments are discussed.

Wheeler, S. G.↗

NASA NTRS1978

Thematic mapper design parameter investigation

This study simulated the multispectral data sets to be expected from three different Thematic Mapper configurations, and the ground processing of these data sets by three different resampling techniques. The simulated data sets were then evaluated by processing them for multispectral classification, and the Thematic Mapper configuration, and resampling technique which provided the best classification accuracy were identified.

Colby, C. P., Jr.↗

NASA NTRS1978

Thematic mapper design parameter investigation

Simulated multispectral data sets used in the Thematic Mapper design parameter investigation are analyzed. The sets were produced by processing high-resolution digital multispectral data through models of the various Thematic Mapper configurations and then through three resampling techniques as a simulation of ground processing. Emphasis is placed on classification processing and analysis of results.

Colby, C. P., Jr.↗

NASA NTRS1977

LANDSAT data from agricultural sites: Crop signature analysis

The LANDSAT multispectral scanner (MSS) data were analyzed with a view toward classification to identify wheat. The notion of spectral signature of a crop, a commonly used basis for classification, was found to be inadequate. Data analysis has revealed that the MSS data from agricultural sites were essentially two dimensional, and that the data from different sites and different acquisition lay on parallel planes in the four dimensional feature space. These results were exploited to gain new insight into the data and to develop alternate models for classification. In particular, it was found that the temporal pattern of change in the spectral response of a crop constitutes its signature and provides a basis for crop classification.

Misra, P. N.↗

NASA NTRS1977

Landsat data from agricultural sites - Crop signature analysis

The Landsat multispectral scanner (MSS) data have been analyzed with a view toward classification to identify wheat. The notion of spectral signature of a crop, a commonly used basis for classification, has been found to be inadequate. Data analysis has revealed that the MSS data from agricultural sites are essentially two dimensional, and that the data from different sites and different acquisitions lie on parallel planes in the four-dimensional feature space. These results have been exploited to gain new insight into the data and to develop alternate models for classification. In particular, it has been found that the temporal pattern of change in the spectral response of a crop constitutes its signature and provides a basis for crop classification.

Misra, P. N.↗

NASA NTRS1976

Linear dimensionality of Landsat agricultural data with implications for classification

A model for the Landsat multispectral scanner data, representing a generalization of the commonly used Gaussian model, has been formulated and analyzed. The model hypothesizes that the data for different crop types essentially lie on distinct hyperplanes in the feature space. Tests of this model reveal that: (1) the agricultural data from any single acquisition (i.e., four-channel) of Landsat are essentially two dimensional, regardless of the crop type; and (2) the data from different sites and different stages of crop development all lie on planes which are parallel. These findings have significant implications for data display, classification, feature extraction, and signature extension.

Wheeler, S. G.↗