34 research outputs found
A discussion of image sharpness
The image sharpness problem is discussed in terms of a scene which is viewed by a sensor. The sensor has some sort of function which is a nonpoint observing function, and it's a function that gathers energy from some region around a point in the scene. That energy is integrated and is contributed to each point or each sample that is taken of the scene. The samples become the pixels, and the pixels are assembled together into the digital image. Each one has some blurr or some of what is called the point's spread function creating the value of information or data that is seen in each pixel. Then the sensor and electronic part of the system acts on that signal out of the sensor and perhaps adds more blurring, more loss of resolution to the system. Finally, sampling and quantization of the data produce their effects. The signal is sampled at some rate and the digital image is created from that. The sampling or digitizing process puts the continuous voltage into a number of discrete binary levels, and another error is introduced there
ERTS multispectral image transformations for geological lineament enhancement
There are no author-identified significant results in this report
Data handling and analysis for the 1971 corn blight watch experiment
The overall corn blight watch experiment data flow is described and the organization of the LARS/Purdue data center is discussed. Data analysis techniques are discussed in general and the use of statistical multispectral pattern recognition methods for automatic computer analysis of aircraft scanner data is described. Some of the results obtained are discussed and the implications of the experiment on future data communication requirements for earth resource survey systems is discussed
LANDSAT-4 image data quality analysis
Classification performance from LANDSAT 4 TM and MSS data is evaluated using the SECHO computer program. The data accuracy is compared using forest, corn, soybeans, bare soil, grass, water, and urban areas as classes for investigation
LANDAT-4/5 image data quality analysis
LANDSAT-4/5 data quality analysis was covered. Focus was on estimation of two-dimensional point-spread function estimation. A brief description is included
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
Spline function approximation techniques for image geometric distortion representation
Least squares approximation techniques were developed for use in computer aided correction of spatial image distortions for registration of multitemporal remote sensor imagery. Polynomials were first used to define image distortion over the entire two dimensional image space. Spline functions were then investigated to determine if the combination of lower order polynomials could approximate a higher order distortion with less computational difficulty. Algorithms for generating approximating functions were developed and applied to the description of image distortion in aircraft multispectral scanner imagery. Other applications of the techniques were suggested for earth resources data processing areas other than geometric distortion representation
Synthetic aperture radar/LANDSAT MSS image registration
Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint
Processing techniques development, volume 3
The author has identified the following significant results. Analysis of the geometric characteristics of the aircraft synthetic aperture radar (SAR) relative to LANDSAT indicated that relatively low order polynominals would model the distortions to subpixel accuracy to bring SAR into registration for good quality imagery. Also the area analyzed was small, about 10 miles square, so this is an additional constraint. For the Air Force/ERIM data, none of the tested methods could achieve subpixel accuracy. Reasons for this is unknown; however, the noisy (high scintillation) nature of the data and attendent unrecognizability of features contribute to this error. It is concluded that the quadratic model would adequately provide distortion modeling for small areas, i.e., 10 to 20 miles square