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Holtzman, J. C.

Publications and source records attributed to Holtzman, J. C..

At least 19 records
All sourcesNASA NTRSDOE OSTI
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

Power spectral ensity of markov texture fields

Texture is an important image characteristic. A variety of spatial domain techniques were proposed for extracting and utilizing textural features for segmenting and classifying images. for the most part, these spatial domain techniques are ad hos in nature. A markov random field model for image texture is discussed. A frequency domain description of image texture is derived in terms of the power spectral density. This model is used for designing optimum frequency domain filters for enhancing, restoring and segmenting images based on their textural properties.

Shanmugan, K. 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

Analysis of geologic terrain models for determination of optimum SAR sensor configuration and optimum information extraction for exploration of global non-renewable resources. Pilot study: Arkansas Remote Sensing Laboratory, part 1, part 2, and part 3

Computer-generated radar simulations and mathematical geologic terrain models were used to establish the optimum radar sensor operating parameters for geologic research. An initial set of mathematical geologic terrain models was created for three basic landforms and families of simulated radar images were prepared from these models for numerous interacting sensor, platform, and terrain variables. The tradeoffs between the various sensor parameters and the quantity and quality of the extractable geologic data were investigated as well as the development of automated techniques of digital SAR image analysis. Initial work on a texture analysis of SEASAT SAR imagery is reported. Computer-generated radar simulations are shown for combinations of two geologic models and three SAR angles of incidence.

Kaupp, V. H.↗
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.↗
NASA NTRS1981

Adaptive filtering of radar images for autofocus applications

Autofocus techniques are being designed at the Jet Propulsion Laboratory to automatically choose the filter parameters (i.e., the focus) for the digital synthetic aperture radar correlator; currently, processing relies upon interaction with a human operator who uses his subjective assessment of the quality of the processed SAR data. Algorithms were devised applying image cross-correlation to aid in the choice of filter parameters, but this method also has its drawbacks in that the cross-correlation result may not be readily interpretable. Enhanced performance of the cross-correlation techniques of JPL was hypothesized given that the images to be cross-correlated were first filtered to improve the signal-to-noise ratio for the pair of scenes. The results of experiments are described and images are shown.

Stiles, J. A.↗
NASA NTRS1981

Evaluation of the soil moisture prediction accuracy of a space radar using simulation techniques

Image simulation techniques were employed to generate synthetic aperture radar images of a 17.7 km x 19.3 km test site located east of Lawrence, Kansas. The simulations were performed for a space SAR at an orbital altitude of 600 km, with the following sensor parameters: frequency = 4.75 GHz, polarization = HH, and angle of incidence range = 7 deg to 22 deg from nadir. Three sets of images were produced corresponding to three different spatial resolutions; 20 m x 20 m with 12 looks, 100 m x 100 m with 23 looks, and 1 km x 1 km with 1000 looks. Each set consisted of images for four different soil moisture distributions across the test site. Results indicate that, for the agricultural portion of the test site, the soil moisture in about 90% of the pixels can be predicted with an accuracy of = + or - 20% of field capacity. Among the three spatial resolutions, the 1 km x 1 km resolution gave the best results for most cases, however, for very dry soil conditions, the 100 m x 100 m resolution was slightly superior.

Ulaby, F. T.↗
NASA NTRS1981

An adaptive filter for smoothing noisy radar images

A spatial domain adaptive Wiener filter for smoothing radar images corrupted by multiplicative noise is presented. The filter is optimum in a minimum mean squared error sense, computationally efficient, and preserves edges in the image better than other filters. The proposed algorithm can also be used for processing optical images with illumination variations that have a multiplicative effect.

Frost, V. S.↗
NASA NTRS1981

A model for simulation and processing of radar images

A model for recording, processing, presentation, and analysis of radar images in digital form is presented. The observed image is represented as having two random components, one which models the variation due to the coherent addition of electromagnetic energy scattered from different objects in the illuminated areas. This component is referred to as fading. The other component is a representation of the terrain variation which can be described as the actual signal which the radar is attempting to measure. The combination of these two components provides a description of radar images as being the output of a linear space-variant filter operating on the product of the fading and terrain random processes. In addition, the model is applied to a digital image processing problem using the design and implementation of enhancement scene. Finally, parallel approaches are being employed as possible means of solving other processing problems such as SAR image map-matching, data compression, and pattern recognition.

Stiles, J. A.↗
NASA NTRS1980

Simulation of orbital radar images

The operating parameters for spaceborne synthetic aperture imaging radar systems was addressed in the cost effective manner by using simulation techniques. The use of airborne images, Seasat images, and computer simulation were the first computer simulation of spaceborne radar imagery was analyzed for system definition studies. Analysis of the simulation indicates that incidence angles as small as 30 are useful for general terrain geomorphologic analysis.

Saunders, R. S.↗