Engineering topics

Landgrebe, David A.

Publications and source records attributed to Landgrebe, David A..

26 records · Page 2

NASA NTRS1991

Parameter trade-offs for imaging spectroscopy systems

With the advent of the EOS era and of configurable sensors, users of these instruments are faced with the twin problems of specifying data acquisition parameters and extracting desired information from the voluminous data. An application of a system model is made to explore system parameter trade-offs for a model sensor based on the High Resolution Imaging Spectrometer. Radiometric performance was studied, along with the effect on classification accuracy of several system parameters. Using a model scene based on typical agricultural reflectance and atmospheric conditions, the atmosphere and sensor are seen to have significant effects on the mean received signal and noise performance. The effect of random uncorrelated errors in the radiometric calibration of the detector array is seen to degrade system performance, especially in the spectral bands below 1 micron. Accurate pixel-to-pixel relative radiometric calibration and the use of the Image Motion Compensation option are seen to improve classification accuracy, especially at high solar zenith angles. Feature sets chosen from characteristics of the scene performed best overall, but ones chosen based on signal-to-noise ratios were seen to be more robust.

Kerekes, John P.↗

NASA NTRS1989

Spectral band selection for classification of soil organic matter content

This paper describes the spectral-band-selection (SBS) algorithm of Chen and Landgrebe (1987, 1988, and 1989) and uses the algorithm to classify the organic matter content in the earth's surface soil. The effectiveness of the algorithm was evaluated comparing the results of classification of the soil organic matter using SBS bands with those obtained using Landsat MSS bands and TM bands, showing that the algorithm was successful in finding important spectral bands for classification of organic matter content. Using the calculated bands, the probabilities of correct classification for climate-stratified data were found to range from 0.910 to 0.980.

Henderson, Tracey L.↗

NASA NTRS1989

A spectral feature design system for the HIRIS/MODIS era

A spectral feature design system for high-dimensional multispectral data is described. This system utilizes hard-limited or infinitely clipped optimal transforms and canonical analysis to extract the spectral features for data volume reduction and classification purposes. The design procedure is intended to be application-specific in order to make it maximally effective for each use. Although it could be used in a variety of circumstances, the procedure was designed with satellite data collection in mind, such as will be needed with the High-Resolution Imaging Spectrometer (HIRIS), a land-oriented Earth observational sensor intended for launch in the mid-1990s. The computation required for the steps prior to the actual satellite data collection are straightforward and could be done with readily available subroutines. The procedure is also defined in such a way as to require only very simple onboard calculations. The tests reported provide substantial data volume reduction in the satellite-to-Earth downlink and subsequent computation phases, while maintaining satisfactory classification accuracy.

Chen, Chih-Chien Thomas↗

NASA NTRS1989

Use of robust estimators in parametric classifiers

The parametric approach to density estimation and classifier design is a well studied subject. The parametric approach is desirable because basically it reduces the problem of classifier design to that of estimating a few parameters for each of the pattern classes. The class parameters are usually estimated using maximum-likelihood (ML) estimators. ML estimators are, however, very sensitive to the presence of outliers. Several robust estimators of mean and covariance matrix and their effect on the probability of error in classification are examined. Comments are made about alpha-ranked (alpha-trimmed) estimators.

Safavian, S. Rasoul↗

NASA NTRS1989

Modeling, simulation, and analysis of optical remote sensing systems

Remote Sensing of the Earth's resources from space-based sensors has evolved in the past 20 years from a scientific experiment to a commonly used technological tool. The scientific applications and engineering aspects of remote sensing systems have been studied extensively. However, most of these studies have been aimed at understanding individual aspects of the remote sensing process while relatively few have studied their interrelations. A motivation for studying these interrelationships has arisen with the advent of highly sophisticated configurable sensors as part of the Earth Observing System (EOS) proposed by NASA for the 1990's. Two approaches to investigating remote sensing systems are developed. In one approach, detailed models of the scene, the sensor, and the processing aspects of the system are implemented in a discrete simulation. This approach is useful in creating simulated images with desired characteristics for use in sensor or processing algorithm development. A less complete, but computationally simpler method based on a parametric model of the system is also developed. In this analytical model the various informational classes are parameterized by their spectral mean vector and covariance matrix. These class statistics are modified by models for the atmosphere, the sensor, and processing algorithms and an estimate made of the resulting classification accuracy among the informational classes. Application of these models is made to the study of the proposed High Resolution Imaging Spectrometer (HRIS). The interrelationships among observational conditions, sensor effects, and processing choices are investigated with several interesting results.

Kerekes, John Paul↗

NASA NTRS1989

HIRIS performance study

The remote sensing system simulation is used to study a proposed sensor concept. An overview of the instrument and its parameters is presented, along with the model of the instrument as implemented in the simulation. Signal-to-noise levels of the instrument under a variety of system configurations are presented and discussed. Classification performance under these varying configurations is also shown, along with relationships between signal-to-noise ratios, feature selection, and classification performance.

Kerekes, John P.↗

NASA NTRS1988

Spectral feature design in high dimensional multispectral data

The High resolution Imaging Spectrometer (HIRIS) is designed to acquire images simultaneously in 192 spectral bands in the 0.4 to 2.5 micrometers wavelength region. It will make possible the collection of essentially continuous reflectance spectra at a spectral resolution sufficient to extract significantly enhanced amounts of information from return signals as compared to existing systems. The advantages of such high dimensional data come at a cost of increased system and data complexity. For example, since the finer the spectral resolution, the higher the data rate, it becomes impractical to design the sensor to be operated continuously. It is essential to find new ways to preprocess the data which reduce the data rate while at the same time maintaining the information content of the high dimensional signal produced. Four spectral feature design techniques are developed from the Weighted Karhunen-Loeve Transforms: (1) non-overlapping band feature selection algorithm; (2) overlapping band feature selection algorithm; (3) Walsh function approach; and (4) infinite clipped optimal function approach. The infinite clipped optimal function approach is chosen since the features are easiest to find and their classification performance is the best. After the preprocessed data has been received at the ground station, canonical analysis is further used to find the best set of features under the criterion that maximal class separability is achieved. Both 100 dimensional vegetation data and 200 dimensional soil data were used to test the spectral feature design system. It was shown that the infinite clipped versions of the first 16 optimal features had excellent classification performance. The overall probability of correct classification is over 90 percent while providing for a reduced downlink data rate by a factor of 10.

Chen, Chih-Chien Thomas↗

NASA NTRS1987

Spectral feature design for data compression in high dimensional multispectral data

Data transmission loads of high dimensional remote sensor systems can be greatly reduced by applying generalized Karhunen-Loeve transform as a feature design technique. Two spectral feature design approaches based upon the generalized K-L transform are developed to compress information effectively. Six sets of field data from Kansas and North Dakota on three different dates each are used to test the methods. Spatially, temporally and spatially/temporally combined data sets are formed in this paper to test the robustness property of the schemes. The probability of correct classification using Landsat MSS, Thematic Mapper bands and the proposed bands are found and compared. The comparison shows that the results are improved by the proposed methods, and they appear to be satisfactorily robust. The overall data compression ratio in this paper is about 100/16, i.e., about 6 to 1 with no loss in classification accuracy.

Chen, C.-C. Thomas↗