Interpolation of ERTS-1 multispectral scanner data

Three interpolation procedures, based on computing values between original sample points, for enlarging a picture are examined. An ERTS frame of Washington, D.C. was used to illustrate the results. Mathematical bases of the interpolation are given

Optimum constrained image restoration filters

The filter was developed in Hilbert space by minimizing the radius of gyration of the overall or composite system point-spread function subject to constraints on the radius of gyration of the restoration filter point-spread function, the total noise power in the restored image, and the shape of the composite system frequency spectrum. An iterative technique is introduced which alters the shape of the optimum composite system point-spread function, producing a suboptimal restoration filter which suppresses undesirable secondary oscillations. Finally this technique is applied to multispectral scanner data obtained from the Earth Resources Technology Satellite to provide resolution enhancement. An experimental approach to the problems involving estimation of the effective scanner aperture and matching the ERTS data to available restoration functions is presented

Constrained optimization of image restoration filters

A linear shift-invariant preprocessing technique is described which requires no specific knowledge of the image parameters and which is sufficiently general to allow the effective radius of the composite imaging system to be minimized while constraining other system parameters to remain within specified limits

On the Assessment of Stability and Patterning of Speech Movements

Speech requires the control of complex movements of orofacial structures to produce dynamic variations in the vocal tract transfer function. The nature of the underlying motor control processes has traditionally been investigated by employing measures of articulatory movements, including movement amplitude, velocity, and duration, at selected points in time. An alternative approach, first used in the study of limb motion, is to examine the entire movement trajectory over time. A new approach to speech movement trajectory analysis was introduced in earlier work from this laboratory. In this method, trajectories from multiple movement sequences are time- and amplitude-normalized, and the STI (spatiotemporal index) is computed to capture the degree of convergence of a set of trajectories onto a single, underlying movement template. This research note describes the rationale for this analysis and provides a detailed description of the signal processing involved. Alternative interpolation procedures for time-normalization of kinematic data are also considered

Analysis of the effects of interpolation and enhancement of LANDSAT-1 Data on classification and area estimation accuracy

There are no author-identified significant results in this report

A parametric multiclass Bayes error estimator for the multispectral scanner spatial model performance evaluation

The author has identified the following significant results. The probability of correct classification of various populations in data was defined as the primary performance index. The multispectral data being of multiclass nature as well, required a Bayes error estimation procedure that was dependent on a set of class statistics alone. The classification error was expressed in terms of an N dimensional integral, where N was the dimensionality of the feature space. The multispectral scanner spatial model was represented by a linear shift, invariant multiple, port system where the N spectral bands comprised the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial, and hence, spectral correlation matrices through the systems, was developed

LANDSAT-4/5 image data quality analysis

A LANDSAT Thematic Mapper (TM) quality evaluation study was conducted to identify geometric and radiometric sensor errors in the post-launch environment. The study began with the launch of LANDSAT-4. Several error conditions were found, including band-to-band misregistration and detector-to detector radiometric calibration errors. Similar analysis was made for the LANDSAT-5 Thematic Mapper and compared with results for LANDSAT-4. Remaining band-to-band misregistration was found to be within tolerances and detector-to-detector calibration errors were not severe. More coherent noise signals were observed in TM-5 than in TM-4, although the amplitude was generally less. The scan direction differences observed in TM-4 were still evident in TM-5. The largest effect was in Band 4 where nearly a one digital count difference was observed. Resolution estimation was carried out using roads in TM-5 for the primary focal plane bands rather than field edges as in TM-4. Estimates using roads gave better resolution. Thermal IR band calibration studies were conducted and new nonlinear calibration procedures were defined for TM-5. The overall conclusion is that there are no first order errors in TM-5 and any remaining problems are second or third order

Experimental Examination of Similarity Measures and Preprocessing Methods Used for Image Registration

An experimental analysis of a number of image registration techniques is described. The objective is to provide a better understanding on a comparative basis of some of the methods of registering imagery that have been proposed. In particular, this study encompasses the choice of a similarity measure and the effects of preprocessing the imagery prior to the registration

Estimation of Modulation Transfer Function of Landsat-4 Sensors from Measured Imagery

Satellite-based multispectral imaging systems have been in operation since 1972 and the latest in the Landsat series of sensors was launched in July 1982. In order to verify that the Landsat-4 Thematic Mapper (TM) and Multispectral Scanner Systems (MSS) sensors are operating within specifications, it is necessary to estimate the system parameters by analysis of the measured data. One parameter of interest is the sensor modulation transfer function (MTF) or the point spread function (PSF) which determines the spatial resolution of the system. This paper discusses two methods to determine the actual overall resolutions after launch. The problem is formulated as a two-dimensional linear system. The measured data can be expressed in the frequency domain as the product of the overall optical transfer function and the spectrum of the earth scene: where F (u,v) is the spectrum of the earth scene, H (u,v) is the optical transfer function and G(u,v) is the spectrum of the measured data

Estimation of a Remote Sensing System Point-Spread Function from Measured Imagery

Satellite-based multispectral imaging systems have been in operation since 1972 and the latest in the Landsat series of sensors was launched in July 1982. One system parameter of interest is resolution and this paper discusses experiments to determine the actual overall resolution after launch. Atmospheric effects and postprocessing effects add to the prelaunch optical resolution. Scene structures, such as roads and field edges, were used with numerical estimation procedures to predict resolution in Landsat-4 Thematic Mapper imagery. A nominal resolution of 39 meters was determined as compared to the predicted 30 m prelaunch value