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Brian P. Bauer

Publications and source records attributed to Brian P. Bauer.

5 recordsLinked to original sources

An automatic optimum kernel-size selection technique for edge enhancement

Edge enhancement is a technique that can be considered, to a first order, a correction for the modulation transfer function of an imaging system. Digital imaging systems sample a continuous function at discrete intervals so that high-frequency information cannot be recorded at the same precision as lower frequency data. Because of this, fine detail or edge information in digital images is lost. Spatial filtering techniques can be used to enhance the fine detail information that does exist in the digital image, but the filter size is dependent on the type of area being processed. A technique has been developed by the authors that uses the horizontal first difference to automatically select the optimum kernel-size that should be used to enhance the edges that are contained in the image.

Remote Sensing of Environment

A survey of image processing developments in support of remote sensing

New algorithm developments for image processing (IP) will occur throughout the 1980's, resulting from evolution in computer hardware and sensors as well as continuing research. This report will describe the areas of algorithm development that are occurring in applications, research, and operational environments. Included is an overview of image processing activities at institutions which are generally regarded as leaders in IP algorithm development and implementation. Finally, this report addresses directions in IP algorithm development that are being proposed for the EROS Data Center (EDC). The major applications of IP at EDC are developed for use in the processing, analysis, and extraction of remote sensing information from Landsat and aircraft data (platforms).

Report

Survey of resampling techniques using MSS and synthetic imagery

The objective of this survey is to investigate the methods of interpolation and deconvolution for image restoration The methods evaluated are nearest neighbor, bilinear interpolation, cubic convolution, and two-dimensional deconvolution. The effects of these restoration methods are demonstrated using Landsat multispectral scanner (MSS) data and synthetic imagery. The effect of these restoration methods are compared as to resolution and spatial frequency effects. The edge effect, a situation that occurs when fill (non-image) data is interpolated with image data, is also addressed.

Report

Considerations for blending data from various sensors

A project is being proposed at the EROS Data Center to blend the information from sensors aboard various satellites. The problems of, and considerations for, blending data from several satellite-borne sensors are discussed. System descriptions of the sensors aboard the HCMM, TIROS-N, GOES-D, Landsat 3, Landsat D, Seasat, SPOT, Stereosat, and NOSS satellites, and the quantity, quality, image dimensions, and availability of these data are summaries to define attributes of a multi-sensor satellite data base. Unique configurations of equipment, storage, media, and specialized hardware to meet the data system requirement are described as well as archival media and improved sensors that will be on-line within the next 5 years. Definitions and rigor required for blending various sensor data are given. Problems of merging data from the same sensor (intrasensor comparison) and from different sensors (intersensor comparison), the characteristics and advantages of cross-calibration of data, and integration of data into a product matrix field are addressed. Data processing considerations as affected by formation, resolution, and problems of merging large data sets, and organization of data bases for blending data are presented. Examples utilizing GOES and Landsat data are presented to demonstrate techniques of data blending, and recommendations for future implementation of a set of standard scenes and their characteristics necessary for optimal data blending are discussed.

Sixth Annual Pecora Symposium and Exposition

Digital and photographic processing study for shallow seas mapping from landsat

The application of contrast stretch and haze removal techniques to Landsat/MSS imagery for shallow seas bathymetry is discussed. The application of these techniques is based upon procedures inherent in the EDIPS system processing. Application of both MSS band 4 and band 5 data are discussed in lx and 3x gain mode. Both quantitative and qualitative (imagery) data are used to demonstrate the existence of bathymetric information after EDIPS processing.

Technical Report