Journal
COMPUTERS AND ELECTRONICS IN AGRICULTURE
Volume 93, Issue -, Pages 55-59Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2013.02.001
Keywords
Airborne multispectral imagery; Remote sensing; Data fusion; Cotton plants
Funding
- Cotton Inc. [10-776)]
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The study investigated the use of aerial multispectral imagery and ground-based hyperspectral data for the discrimination of different crop types and timely detection of cotton plants over large areas. Airborne multispectral imagery and ground-based spectral reflectance data were acquired at the same time over three large agricultural fields in Burleson Co., Texas during the 2010 growing season. The discrimination accuracy of aerial- and ground-based data was examined individually; then a multi-sensor data fusion technique was applied on both datasets in order to improve the accuracy of discrimination. The individual classification accuracy of data taken with the aerial- and ground-based sensors were 90% and 93.3%, respectively. In comparison, the accuracy of discriminating crop types with fused data was 100% in the calibration and only 3.33% misclassification in the cross-validation. These results suggest that data fusion techniques could greatly enhance our ability to detect cotton from other plants. Published by Elsevier B.V.
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