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Eyton, J. R.

Publications and source records attributed to Eyton, J. R..

Digital image data sets for remote sensing instruction

Several sets of LANDSAT image segments and a set of simple image processing programs operating in batch mode are being used to teach an introductory course in digital image analysis and classification. The image data sets contain only a small number of pixels and can be run quickly on most university computing systems. A number of image data sets derived from the raw spectral bands (smoothed data sets, band ratios and texture data sets) allows the student to examine and include these extracted variables in the classification process. Another data set consists of three registered LANDSAT image segments for three different dates so that the student may experiment with multitemporal data processing. The three LANDSAT scenes used in the class are described and the image data sets available for class exercises are listed.

Eyton, J. R.

Crop classification with a Landsat/radar sensor combination

A combined Landsat/radar approach to classification of remotely sensed data, with emphasis on crops, was undertaken. Radar data were obtained by microwave radar spectrometers over fields near Eudora, Kansas and Landsat image data were obtained for the same test site. After Landsat digital images were registered and test-cells extracted, a comparable set of radar image pixels were simulated to match the Landsat pixels. The combined data set is then used for classification, and the results are examined with the best combination of sensor variables identified. Finally, the usefulness of radar in a simulated cloud-cover situation is demonstrated. The major conclusion derived from this study is that the combination of radar/optical sensors is superior to either one alone.

Li, R. Y.