Calibration of the University of Michigan Aircraft Multispectral Scanner Data Using Smoothed Calibration Coefficients

A preprocessor (SMCAL) is described which produces a data tape which is calibrated in any desired calibration code with smoothed calibration coefficients. The C1-C0 and C2-C0 values used to gain calibrate each line of data are linearly smoothed over nine lines before and after each line

A Parametric Model for Multispectral Scanners

Efficient acquisition and utilization of remotely sensed data requires an extensive a priori evaluation of the performance of the basic data collection unit, the multispectral scanner. The objective is the development of a fully parametric technique to theoretically evaluate the systems response in any desired operational environment and provide the necessary information in selecting a set of optimum parameters. In this paper the multispectral scanner spatial characteristics are represented by a linear shift-invariant multiple-port system where the N spectral bands comprise the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial and hence spectral correlation matrices through the systems, is developed. Specific cases for Gaussian point spread functions are examined. The integration of the scanner spatial model and a parameter classification error estimator provides the necessary technique to evaluate the performance of a multispectral scanner. A set of test statistics are specified and the corresponding output quantities computed by the characteristic function. Two sets of classification accuracies, one at the input and one at the output are estimated. The scanner\u27s instantaneous field of view is changed and the variation of the output classification performance monitored

Geometric Correction of ERTS-1 Digital Multispectral Scanner Data

ERTS-1 Multispectral Scanner Data is received from the satellite by NASA, processed, and delivered to users recorded on computer compatible tape and in photographic form. The computer tape form of the data is calibrated and line length adjusted by NASA but no geometric corrections are applied. The system-corrected photographic products are corrected for many geometric distortions including earth rotation effects in addition to the above two corrections. Also, these images are rescaled so that the horizontal and vertical scales are the same. Thus, the digital form of the MSS data contains many geometric distortions and users of this data are faced with the problem of compensating for these errors

A Cluster-Oriented Analysis of Multispectral Scanner Data

The standard procedure at LARS for classification analysis of aircraft scanner data has been to training samples (fields) based on the ground truth data and to use these directly or in a form subdivided via clustering as training for the computer. In this process the major emphasis is on defining class statistics which describe actual materials of interest as designated by the user. The approach assumes these materials are spectrally separable and classification is performed to test if this is true. In the approach described here the multispectral data is first clustered to determine what spectrally separable groups exist in the data. After these separable groups are found they are related to their physical meaning or ground truth . This approach is in a sense the reverse of ground truth oriented training procedure referred to above. There are two requirements often cited for multispectral pattern recognition to be useful for a particular material in the scene. First it must be spectrally separable from all the others in the scene and, two, it must be of informational value. In the existing approach the features which are of informational value are first defined. In the cluster oriented approach the spectrally separable classes are found first and time consuming training and test classification analysis to determine the spectral separability of unseparable materials is avoided

Calibration of Aircraft Scanner Data Using Ground Reflectance Panels

An experiment is described in which aircraft scanner data from calibrated reflectance panels in the scene was used to calibrate the scanner data for nearby targets. The method used permits reflectance calibration of scanner data for areas which are in environmental proximity to the reflectance panels. That is, the calibration is valid for areas receiving the same illumination, from the same sun angle and for the same aircraft altitude. Atmospheric condition changes, cloud cover changes, different sun angles, and other environmental factor changes will alter the calibration and render the panel reference parameters invalid. The method described here is experimental and is disclosed for the use of LARS researchers wishing to experiment with estimation of approximate reflectance data even though it includes the variances caused by the environmental factors

Sun Angle Effect Preprocessing with Predicted Ramp Functions

Among the many factors affecting the LARS multispectral scanner data is the geometric interaction of the sensor look angle and solar illumination angle. One of these interactions is quite well known to many LARS researchers as the sun-angle effect to some and solar ramping or just ramping to others. Ramping is the in mean scanner response as a function of scanner look angle for a given data set; or, the change in column mean from column to column for a given run

Estimation of Sampling Requirements for Track-Type Remote Sensing Surveys

Many types of remote sensing measurements are made along lines or tracks over the earth\u27s surface which are spaced at distances governed primarily by assumptions about the nature of the phenomena being measured and cost considerations. In geophysical surveys, aircraft-borne magnetic, gravity, gamma ray, electromagnetic and other sensors are flown at low altitude with approximately parallel line spacings ranging from 1/4-mile to several miles. These measurements are commonly sampled and digitized at an arbitrarily high rate along the flight path generating an adequately sampled record with respect to the Nyquist rate which is governed by the bandwidth of the physical phenomenon being observed. The sampling interval in the across track direction is the track spacing and closely spaced samples there would be extremely costly to obtain because of the increased number of flight lines required. The research discussed in this paper addresses the problem of determining the sampling requirements for proper representation of the geophysical fields and is based on study of the power spectral density of the measured quantities. The primary object of the measurement of various geophysical phenomena in exploration for minerals and petroleum deposits is to locate anomalies in these variables which may relate to targets of economic value. The spacing of survey lines has an important impact on the ability to reconstruct the measured surface and subsequent detection of anomalies. A method of selecting line spacing is discussed, in which the along track spectrum is used to predict the across tract frequency content of the scene using certain assumptions on the isotropy of the fields of interest. Comparisons will be shown of spectral estimation using classical windowed periodogram and autoregressive methods. Analysis results using data from analytical models and real data from U.S. Energy Research and Development Administration airborne geophysical surveys will be presented

Analysis of Geophysical Remote Sensing Data Using Multivariate Pattern Recognition Techniques

Multivariate statistical pattern recognition techniques have been widely used in the analysis of multispectral scanner remote sensing data for crop surveys, forest mapping, land use surveys and in many other applications. These applications are restricted basically to surface cover reflectance and emissivity phenomena. In the study described in this paper multivariate analysis techniques were applied to geophysical remote sensing data which measures phenomena occurring beneath the surface of the earth. Three types of geophysical data: magnetic anomaly, induced pulse transient, and gamma ray data were digitized, registered and analyzed to observe relationships to known geology. In addition several types of surficial remote sensing data including LANDSAT multispectral scanner, side looking airborne radar (SLAR) and thermal infrared scanner data were included in the multivariate data set to enable evaluation of all the available remote sensing variables. The preprocessing and analysis techniques are discussed and results showing correlations between variables and relationships to geology is presented

Effects of Spatial Distortion on Image Registration Performance

It is frequently desired to accurately register or overlay different images of the same scene. Under conditions in which the sensor-to-scene geometry is exactly the same for both images, the registration is accomplished by simply determining the relative translation between the images. However, in many instances there are variations in the sensor-to-scene geometry which cause the images to be spatially distorted relative to one another. This condition requires that a spatial warping be applied to one of the images in addition to determining the relative translation between the images in order to achieve registration. For many situations in which the relative spatial distortion is small (e-g., temporally differing LANDSAT images), it is assumed that the spatial differences are negligible for small subimages. Registration is accomplished by overlaying corresponding subimages within each of the images via translation only and then applying a spatial warping to one of the images based upon the subimage registrations. One of the primary parameters that must be determined in such a procedure is the subimage size to be used for registration. The analysis carried out presents a method by which the optimum image size may be chosen based upon a model of the spatial distortion and the premise that the registration processor is designed to overlay spatially congruent images. This is done by determining the effect of the relative spatial distortion upon the output signal-to-noise ratio of the registration processor. It is shown that for spatial distortions which increase with image size (e.g., rotation, scale difference), there is an optimal image size which maximizes the output signal-to-noise ratio. The analysis is also applied to a model of spatial distortions between temporally differing LANDSAT images