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Optical Engineering • Open Access

Discriminating crop and other canopies by overlapping binary image layers
Author(s): Ryoichi Doi

Paper Abstract

For optimal management of agricultural fields by remote sensing, discrimination of the crop canopy from weeds and other objects is essential. In a digital photograph, a rice canopy was discriminated from a variety of weed and tree canopies and other objects by overlapping binary image layers of red-green-blue and other color components indicating the pixels with target canopy-specific (intensity) values based on the ranges of means ±(3×) standard deviations. By overlapping and merging the binary image layers, the target canopy specificity improved to 0.0015 from 0.027 for the yellow 1× standard deviation binary image layer, which was the best among all combinations of color components and means ±(3×) standard deviations. The most target rice canopy-likely pixels were further identified by limiting the pixels at different luminosity values. The discriminatory power was also visually demonstrated in this manner.

Paper Details

Date Published: 25 January 2013
PDF: 4 pages
Opt. Eng. 52(2) 020502 doi: 10.1117/1.OE.52.2.020502
Published in: Optical Engineering Volume 52, Issue 2
Show Author Affiliations
Ryoichi Doi, The Univ. of Tokyo (Japan)


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