UAV-Based Wetland Monitoring: Multispectral and Lidar Fusion with Random Forest Classification
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Ground and aerial imaging techniques to detect tree damage caused by bark beetles in forested areas
Remote sensing technology of multiband color photography for earth resources applications
Photointerpretation of high altitude small scale aerial photography to identify major vegetation type boundaries and predominant forest species
Detrimental atmospheric path effects on spectral signals of remote imaging sensor development for life detection
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Facsimile reproducer records spatially well-registered true-color and false-color video data on tape. Proposed optical arrangement allows light sources to be adjusted along the mirror scanning direction in such a manner that no electronic delays are required if the reproducer is operated synchronously with the camera.
Remote sensing techniques for water resources evaluation and hydrobiological features mapping with multiband scanner imagery
Device permitting simultaneous viewing of infrared images at different wavelengths consists of imaging lens, Michelson interferometer, array of infrared detectors, data processing equipment for Fourier transformation of detector signal, and image display unit. Invention is useful in earth resources applications, nondestructive testing, and medical diagnoses.
Digital computer processing of visible and reflective infrared scanner data for automatic computer mapping of Yellowstone National Park
The possibility of improving the accuracy of terrain classification by preprocessing spectral data was investigated. Terrain maps were made using the following techniques: 1) preprocessing by scan angle function transformation, using the computer-selected best set of three channels; and 2) preprocessing by ratio transformation, using the specified ERTS data channels, simulated by fitting the spectral response of each of the 12 data channels to the ERTS channels by a set of weighting coefficients. By using a simple technique during printout, the maps were produced in color. The normalized scan angle function transformation resulted in the most accurate classification. The best ratio transformation for the Yellowstone Park data was the ratio of each channel to the sum of all channels. A supervised training program involving maximum likelihood decision for selecting the best spectrometer channels and similar techniques for digitizing the data of the analog magnetic tapes were used. Cloud shadows were recognized in addition to eight classes of terrain. Preprocessing of data resulted in more accurate maps, required fewer training areas (hence less preparation and computer time), and enabled much of the area formerly classified as shadow to be reclassified according to actual terrain type.
Two statistical models are compared in the classification of crops recorded on color aerial photographs. A theory of error ellipses is applied to the pattern recognition problem. An elliptical boundary condition classification model (EBC), useful for recognition of candidate patterns, evolves out of error ellipse theory. The EBC model is compared with the minimum distance to the mean (MDM) classification model in terms of pattern recognition ability. The pattern recognition results of both models are interpreted graphically using scatter diagrams to represent measurement space. Measurement space, for this report, is determined by optical density measurements collected from Kodak Ektachrome Infrared Aero Film 8443 (EIR). The EBC model is shown to be a significant improvement over the MDM model.
There are no author-identified significant results in this report.
There are no author-identified significant results in this report.
There are no author-identified significant results in this report.