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At least 19 records

Higher-order neural network software for distortion invariant object recognition

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

Reid, Max B.

Recognition of surface lithologic and topographic patterns in southwest Colorado with ADP techniques

Analysis of ERTS-1 multispectral data by automatic pattern recognition procedures is applicable toward grappling with current and future resource stresses by providing a means for refining existing geologic maps. The procedures used in the current analysis already yield encouraging results toward the eventual machine recognition of extensive surface lithologic and topographic patterns. Automatic mapping of a series of hogbacks, strike valleys, and alluvial surfaces along the northwest flank of the San Juan Basin in Colorado can be obtained by minimal man-machine interaction. The determination of causes for separable spectral signatures is dependent upon extensive correlation of micro- and macro field based ground truth observations and aircraft underflight data with the satellite data.

Melhorn, W. N.

Recognition of surface lithologic and topographic patterns in southwest Colorado with ADP techniques

Analysis of ERTS-1 multispectral data by automatic pattern recognition procedures is applicable toward grappling with current and future resource stresses by providing a means for refining existing geologic maps. The procedures used in the current analysis already yield encouraging results toward the eventual machine recognition of extensive surface lithologic and topographic patterns. Automatic mapping of a series of hogbacks, strike valleys, and alluvial surfaces along the northwest flank of the San Juan Basin in Colorado can be obtained by minimal man-machine interaction. The determination of causes for separable spectral signatures is dependent upon extensive correlation of micro- and macro field based ground truth observations and aircraft underflight data with the satellite data.

Melhorn, W. N.

Interactive display/graphics systems for remote sensor data analysis

A color-television display system and interactive graphics equipment on-line to an IBM 360/44 computer are used to develop a variety of interactive displays which aid in analyzing remote sensor data. These interactive displays are used to: (1) analyze data from a multispectral scanner; (2) develop automatic pattern recognition systems based on multispectral scanner measurements; and (3) analyze data from non-imaging sensors such as the infrared radiometer and microwave scatterometer.

Eppler, W. G.

Detection of southern corn leaf blight by remote sensing techniques.

Multispectral photographic and scanner data were collected over western Indiana in August and September 1970, to determine the detectability of southern corn leaf blight by remote sensing. Measurements were made at altitudes of 3000 to 7000 ft. Color, color IR, and multiband black and white photography were collected at altitudes from 3000 to 60,000 ft. Six levels of infection based on the amount of leaf damage were identified in the fields. Three levels of infection were detected with color IR photography by standard photo-interpretive techniques. Up to five levels of infection were distinguished by applying automatic pattern recognition techniques to the multispectral scanner data. The results illustrate the potential of remote sensing techniques in the detection of crop diseases.

Bauer, M. E.

Interactive display/graphics systems for remote sensor data analysis.

Using a color-television display system and interactive graphics equipment on-line to an IBM 360/44 computer, investigators at the Manned Spacecraft Center have developed a variety of interactive displays which aid in analyzing remote sensor data. This paper describes how such interactive displays are used to: (1) analyze data from a multispectral scanner, (2) develop automatic pattern recognition systems based on multispectral scanner measurements, and (3) analyze data from nonimaging sensors such as the infrared radiometer and microwave scatterometer.

Eppler, W. G.

Statistical separability of agricultural cover types in subsets of one to twelve spectral channels

The purpose of this study was to determine the statistical separability of multispectral measurements from agricultural cover types: corn, soybeans, green forage (hay and pasture) and forest, in one to twelve spectral channels. Multispectral scanner data in twelve spectral channels in the wavelength range 0.4 to 11.7 microns, acquired for three flightlines were analysed by applying automatic pattern recognition techniques. The same analysis was performed for the data acquired a month later over the same three flightlines to investigate the effect of time on statistical separability of agricultural cover types. In the subsets of one to six spectral channels, the combination of wavelength regions (where V, N, M and T denote the visible, near infrared, middle infrared and thermal infrared wavelength regions, respectively): V, V M, V N M, V N M T, V V N M T, V V N M M T, respectively, were found to be the best choices for getting good overall statistical separability of the agricultural cover types for the data acquired.

Kumar, R.

Urban land use: Remote sensing of ground-basin permeability

A remote sensing analysis of the amount and type of permeable and impermeable surfaces overlying an urban recharge basin is discussed. An effective methodology for accurately generating this data as input to a safe yield study is detailed and compared to more conventional alternative approaches. The amount of area inventoried, approximately 10 sq. miles, should provide a reliable base against which automatic pattern recognition algorithms, currently under investigation for this task, can be evaluated. If successful, such approaches can significantly reduce the time and effort involved in obtaining permeability data, an important aspect of urban hydrology dynamics.

Tinney, L. R.

Evaluation of spectral channels and wavelength regions for separability of agricultural cover types

The spectral channels were evaluated as well as wavelength regions; visible, near infrared, middle infrared and thermal infrared were evaluated with respect to their estimated probability of correct classification (P sub c) in discriminating agricultural cover types. Multispectral scanner data in twelve spectral channels in the wavelength range of 0.4 to 11.7 micrometers acquired in the middle of July for three flightlines were analyzed by applying automatic pattern recognition techniques. The same analysis was performed for the data acquired in the middle of August, over the same three flightlines, to investigate the effect of time on the results. The effect of deletion of each spectral channel as well as each wavelength region on P sub c was given. Values of P sub c for all possible combinations of wavelength regions in the subsets of one to twelve spectral channels were also given. The overall values of P sub c were found to be greater for the data of the middle of August than the data of the middle of July.

Kumar, R.

Evaluation of wavelength groups for discrimination of agricultural cover types

Multispectral scanner data in twelve spectral channels, in the wavelength range 0.46 to 11.7 mm, acquired in July 1971 for three flightlines, were analyzed by applying automatic pattern recognition techniques. These twelve spectral channels were divided into four wavelength groups (W1, W2, W3 and W4), each consisting of three wavelength channels -- with respect to their estimated probability of correct classification (P sub c) in discriminating agricultural cover types. The same analysis was also done for the data acquired in August, to investigate the effect of time on these results. The effect of deletion of each of the wavelength groups on P sub C in the subsets of one to nine channels, is given. Values of P sub C for all possible combinations of wavelength groups, in the subsets of one to eleven channels are also given.

Kumar, R.

Evaluation of spectral channels and wavelength regions for separability of agricultural cover types

The purpose of this study was to evaluate the spectral channel as well as wavelength regions - visible, near infrared, middle infrared and thermal infrared - with respect to their estimated probability of correct classification (P-c) in discriminating agricultural cover types. Multispectral scanner data in twelve spectral channels in the wavelength range of 0.4 to 11.7 micron acquired in the middle of July for three flightlines were analysed by applying automatic pattern recognition techniques. The same analysis was performed for the data acquired in the middle of August, over the same three flightlines, to investigate the effect of time on the results. The effect of deletion of each spectral channel as well as each wavelength region on P-c is given. Values of P-c for all possible combinations of wavelength regions in the subsets of one to twelve spectral channels are also given. The overall values of P-c were found to be greater for the data of the middle of August than the data of the middle of July.

Kumar, R.

Separability of agricultural cover types in spectral channels and wavelength regions

Spectral channels and wavelength regions (visible, near infrared, middle infrared and thermal infrared) were evaluated with respect to their estimated probability of correct classification (Pc) in discriminating agricultural cover types. Multispectral scanner data in twelve spectral channels in the wavelength range of 0.4 to 11.7 microns acquired in the middle of July for three flightlines, were analyzed by applying automatic pattern recognition techniques. The same analysis was performed for the data acquired in the middle of August, 1971, over the same three flightlines, to investigate the effect of time on the results. The effect of deletion of each spectral channel as well as each wavelength region on Pc is given. Values of Pc for all possible combinations of wavelength regions in the subsets of one to twelve spectral channels are also given. The overall values of Pc were found to be greater for the data of the middle of August than the data of the middle of July.

Kumar, R.

Evaluation of spectral channels and wavelength regions for separability of agricultural cover types

The author has identified the following significant results. Multispectral scanner data in twelve spectral channels in the wavelength range of 0.4 to 11.7 microns acquired in the middle of July for three flightlines were analyzed by applying automatic pattern recognition techniques. The same analysis was performed for the data acquired in mid August, over the same three flightlines, to investigate the effect of time on the results. The effect of deletion of each spectral channel, as well as each wavelength region on P sub c, is given. Values of P sub c for all possible combinations of wavelength regions in the subsets of one to twelve spectral channels are also given. The overall values of P sub c were found to be greater for the data of mid August than the data from mid July.

Dejesusparada, N.

Evaluation of wavelength groups for discrimination of agricultural cover types

Multispectral scanner data in twelve spectral channels, in the wavelength range 0.46 to 11.7 microns, acquired in July, 1971, for three flightlines, were analyzed by applying automatic pattern recognition techniques. These twelve spectral channels were divided into four wavelength groups (W1, W2, W3 and W4), each consisting of three wavelength groups - with respect to their estimated probability of correct classification (Pc) - in discriminating agricultural cover types. The same analysis was also done for the data acquired in August, to investigate the effect of time on these results. The effect of deletion of each of the wavelength groups on Pc, in the subsets of one to nine channels, is given. Values of Pc for all possible combinations of wavelength groups, in the subsets of one to eleven channels, are also given.

Kumar, R.

Separability of agricultural cover types in spectral channels and wavelength regions

This study was a continuation of a more complete evaluation of the spectral channels as well as wavelength regions - visible, near infrared, middle infrared, and thermal infrared - with respect to their estimated probability of correct classification Pc in discriminating agricultural cover types reported previously by Kumar and Silva (1977). Multispectral scanner data in twelve spectral channels in the wavelength range of 0.4-11.7 microns acquired in the middle of July for three flightlines were analyzed by applying automatic pattern recognition techniques. The same analysis was performed for the data acquired in the middle of August 1971, over the same three flightlines, to investigate the effect of time on the results. The effect of deletion of each spectral channel as well as each wavelength region on Pc is given. Values of Pc for all possible combinations of wavelength regions in the subsets of one to twelve spectral channels are also given. The overall values of Pc were found to be greater for the data of the middle of August than the data of the middle of July.

Kumar, R.