Search NASA⌕ Search

Engineering topics

Frost, V. S.

Publications and source records attributed to Frost, V. S..

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Geological mapping potential of computer-enhanced images from the Shuttle Imaging Radar - Lisbon Valley Anticline, Utah

Computer-enhancement techniques applied to the SIR-A data from the Lisbon Valley area in the northern portion of the Paradox basin increased the value of the imagery in the development of geologically useful maps. The enhancement techniques include filtering to remove image speckle from the SIR-A data and combining these data with Landsat multispectral scanner data. A method well-suited for the combination of the data sets utilized a three-dimensional domain defined by intensity-hue-saturation (IHS) coordinates. Such a system allows the Landsat data to modulate image intensity, while the SIR-A data control image hue and saturation. Whereas the addition of Landsat data to the SIR-A image by means of a pixel-by-pixel ratio accentuated textural variations within the image, the addition of color to the combined images enabled isolation of areas in which gray-tone contrast was minimal. This isolation resulted in a more precise definition of stratigraphic units.

Curlis, J. D.↗
NASA NTRS1986

A data compression technique for synthetic aperture radar images

A data compression technique is developed for synthetic aperture radar (SAR) imagery. The technique is based on an SAR image model and is designed to preserve the local statistics in the image by an adaptive variable rate modification of block truncation coding (BTC). A data rate of approximately 1.6 bit/pixel is achieved with the technique while maintaining the image quality and cultural (pointlike) targets. The algorithm requires no large data storage and is computationally simple.

Frost, V. S.↗
NASA NTRS1985

Maximum likelihood classification of synthetic aperture radar imagery

Classification of synthetic aperture radar (SAR) images has important applications in geology, agriculture, and the military. A statistical model for SAR images is reviewed and a maximum likelihood classification algorithm developed for the classification of agricultural fields based on the model. It is first assumed that the target feature information is known a priori. The performance of the algorithm is then evaluated in terms of the probability of incorrect classification. A technique is also presented to extract the needed feature information from a SAR image; then both the feature extraction and the maximum likelihood classification algorithms are tested on a SEASAT-A SAR image.

Frost, V. S.↗
NASA NTRS1985

Optimal frequency domain textural edge detection filter

An optimal frequency domain textural edge detection filter is developed and its performance evaluated. For the given model and filter bandwidth, the filter maximizes the amount of output image energy placed within a specified resolution interval centered on the textural edge. Filter derivation is based on relating textural edge detection to tonal edge detection via the complex low-pass equivalent representation of narrowband bandpass signals and systems. The filter is specified in terms of the prolate spheroidal wave functions translated in frequency. Performance is evaluated using the asymptotic approximation version of the filter. This evaluation demonstrates satisfactory filter performance for ideal and nonideal textures. In addition, the filter can be adjusted to detect textural edges in noisy images at the expense of edge resolution.

Townsend, J. K.↗
NASA NTRS1985

Information for space radar designers: Required dynamic range vs resolution and antenna calibration using the Amazon rain forest

Calibration of the vertical pattern of the antennas for the SEASAT scatterometer was accomplished using the nearly-uniform radar return from the Amazon rain forest. A similar calibration will be attempted for the SIR-B antenna. Thick calibration is important to establish the radiometric calibration across the swath of the SIR-B, and the developed methodology will provide an important tool in the evaluation of future spaceborne imaging radars. This calibration was made by the very-wide-beam SEASAT scatterometer antennas because at 14.65 GHz the scattering coefficient of the rain forest is almost independent of angle of incidence. It is expected that the variation in scattering coefficient for the rain forest across the relatively narrow vertical beam of the SIR-B will be very small; even at L band the forest should be essentially impenetrable for radar signals, the volume scatter from the treetops will predominate as at higher frequencies. The basic research elements include: (1) examination of SIR-B images over the rain forest to establish the variability of the scattering coefficient at finer resolutions than that of the SEASAT scatterometer; (2) analysis of the variability of SIR-B data detected prior to processing for either azimuth compression or; possibly, range compression so that averages over relatively large footprints can be used; (3) processing of data of the form of (2) using algorithms that can recover the vertical pattern of the antenna.

Moore, R. K.↗
NASA NTRS1984

Information extraction and transmission techniques for spaceborne synthetic aperture radar images

Information extraction and transmission techniques for synthetic aperture radar (SAR) imagery were investigated. Four interrelated problems were addressed. An optimal tonal SAR image classification algorithm was developed and evaluated. A data compression technique was developed for SAR imagery which is simple and provides a 5:1 compression with acceptable image quality. An optimal textural edge detector was developed. Several SAR image enhancement algorithms have been proposed. The effectiveness of each algorithm was compared quantitatively.

Frost, V. S.↗
NASA NTRS1984

The influence of sensor and flight parameters on texture in radar images

Texture is known to be important in the analysis of radar images for geologic applications. It has previously been shown that texture features derived from the grey level co-occurrence matrix (GLCM) can be used to separate large scale texture in radar images. Here the influence of sensor parameters, specifically the spatial and radiometric resolution and flight parameters, i.e., the orientation of the surface structure relative to the sensor, on the ability to classify texture based on the GLCM features is investigated. It was found that changing these sensor and flight parameters greatly affects the usefulness of the GLCM for classifying texture on radar images.

Frost, V. S.↗
NASA NTRS1984

Probability of error and radiometric resolution for target discrimination in radar images

A straightforward statistical analysis is presented of the problem of discriminating between two distributed targets in a radar image. The probability of making an error is calculated as a function of the target contrast, the number of samples averaged to form each observation, and the system noise. In the absence of system noise, it is observed that a target contrast of 1 dB could not be detected with less than 10 samples averaged; it is also seen that the error probability is sensitive to the number of samples averaged, as expected. Setting the target contrast at the radiometric resolution, a comparison is made of different definitions for this resolution. The comparison shows that each definition yields a different error probability, ranging from less than 0.05 to 0.35. Attention is also given to the effect of system noise, and it is shown that thermal noise does not have a significant effect on the error probability for a fixed target contrast.

Frost, V. S.↗
NASA NTRS1983

The information content of synthetic aperture radar images of terrain

A statistical model is developed that portrays an imaging radar as a noisy communication channel with multiplicative noise, and the model is used to evaluate the average amount of information that can be extracted about a target from its radar image. The average information content is also used to define a measure of radiometric resolution for radar images. It is shown that the information content and the resolution capabilities of an imaging radar reach a limit beyond which an increase in scene dynamic range does not improve the information content or the resolution. This limitation results from the multiplicative nature of the noise introduced in the imaging process.

Frost, V. S.↗
NASA NTRS1983

Digital enhancement of SAR imagery as an aid in geologic data extraction

The geological data content of Seasat A SAR imagery was assessed by correlating images of the Southern Appalachians with optical and digital techniques using a digital enhancement algorithm. The evaluation was performed in terms of lithology, lineaments, and geological structure. Digital correlation of the images was found to be more effective than optical correlation as a geological mapping instrument when considered in the light of ground truth data. The digital enhancement algorithm consists of a mean square error analysis which preserves the edge structure in the SAR imagery and decreases the noise content. Additionally, digital correlations allowed for faster computer processing of the imagery.

Frost, V. S.↗
NASA NTRS1983

The influence of sensor and flight parameters on texture in radar images

Texture is known to be important in the analysis of radar images for geologic applications. It has previously been shown that texture features derived from the grey level co-occurrence matrix (GLCM) can be used to separate large scale texture in radar images. Here the influence of sensor parameters, specifically the spatial and radiometric resolution and flight parameters, i.e., the orientation of the surface structure relative to the sensor, on the ability to classify texture based on the GLCM features is investigated. It was found that changing these sensor and flight parameters greatly affects the usefulness of the GLCM for classifying texture on radar images.

Frost, V. S.↗
NASA NTRS1983

The Influence of Sensor and Flight Parameters on Texture in Radar Images

Texture is known to be important in the analysis of radar images for geologic applications. It was previously shown that texture features derived from the grey-level co-occurrence matrix (GLCM) can be used to separate large scale texture in radar images. The influence of sensor parameters, specifically the spatial and radiometric resolution and flight parameters, i.e., the orientation of the surface structure relative to the sensor, on the ability to classify texture based on the GLCM features is investigated. It was found that changing these sensor and flight parameters greatly affects the usefulness of the GLCM for classifying texture on radar images.

Frost, V. S.↗
NASA NTRS1982

The recognition of extended targets - SAR images for level and hilly terrain

Radar image simulation techniques are used to determine the character of SAR images of area-extensive targets, for the cases of flat underlying terrain and of moderate relief, with a view to the severity of elevation change effects on the detection and recognition of boundaries and shapes. The experiment, which demonstrated these effects on shapes and boundaries, was performed in order to establish Seasat-A SAR performance. Attention is given to the geometry/propagation effects in range perspective imaging that must be known for information extraction.

Stiles, J. A.↗
NASA NTRS1982

A model for radar images and its application to adaptive digital filtering of multiplicative noise

Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.

Frost, V. S.↗
NASA NTRS1982

A statistical model for radar images of agricultural scenes

The presently derived and validated statistical model for radar images containing many different homogeneous fields predicts the probability density functions of radar images of entire agricultural scenes, thereby allowing histograms of large scenes composed of a variety of crops to be described. Seasat-A SAR images of agricultural scenes are accurately predicted by the model on the basis of three assumptions: each field has the same SNR, all target classes cover approximately the same area, and the true reflectivity characterizing each individual target class is a uniformly distributed random variable. The model is expected to be useful in the design of data processing algorithms and for scene analysis using radar images.

Frost, V. S.↗
NASA NTRS1982

An information theory characterization of radar images and a new definition for radiometric resolution

The noise properties of the radar image formation process are used in the present modeling of a communication channel in which the desired target properties are the information transmitted, and the final image represents the received signal. The average information rate over this communication channel is calculated together with appropriate bounds and approximations, and is found to be small on a per-sample basis. As a result, many samples must be averaged to allow for the discrimination, or classification, of several levels of target reflectivity. These information rate properties are consistent with known results concerning target detection and image quality in speckle, and the rate is applicable to the definition of radar image radiometric resolution. Radiometric resolution is functionally related to the degree of noncoherent averaging performed by the sensor.

Frost, V. S.↗
NASA NTRS1982

Edge detection for synthetic aperture radar and other noisy images

The development is examined of a new edge detector which is shown to perform adequately in the non-Gaussian multiplicative noise environment which characterizes radar images. This edge detector operates over larger local neighborhood and is less susceptible to noise than previous edge detectors and is therefore more suitable for radar. In addition, a radar image noise model is employed for the design of this new operator. This edge detector is unique in that it is assumed that every local area belongs to either the class of local areas not containing edges or to the class of local areas containing edges. Each pixel's local neighborhood is then assigned to one of these two classes using a statistical hypothesis (a likelihood ratio) test. It is demonstrated that this algorithm is useful for detecting edges in radar images.

Frost, V. S.↗
NASA NTRS1981

Textural features for radar image analysis

Texture is seen as an important spatial feature useful for identifying objects or regions of interest in an image. While textural features have been widely used in analyzing a variety of photographic images, they have not been used in processing radar images. A procedure for extracting a set of textural features for characterizing small areas in radar images is presented, and it is shown that these features can be used in classifying segments of radar images corresponding to different geological formations.

Shanmugan, K. S.↗