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SELECTION AND TESTING OF SPECTRAL BANDS FOR EDGE-BASED LEAF SEGMENTATION

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:   No Citation available.
Authors:   Scott D Noble, Ralph B Brown
Keywords:   segmentation, hyperspectral imaging, edge detection, band selection

Leaf shape is an important feature used in plant identification. The first step in automating leaf shape analysis in complex, real-world scenes is to segment individual leaves. Leaves are often occluded and overlapping, and the similar colour and texture characteristics of the leaves complicate the task of separating them. A segmentation approach based on simple edge detectors operating on narrow-waveband images from an imaging spectrophotometer was developed and tested. Band selection was done by comparing separability of leaf-leaf, vein, and leaf-overlap edge regions of multi-plant, images for four species in 115 spectral bands between 400 and 1000 nm. Testing resulted in a mean segmentation percentage of 63% using a Sobel edge detector operating on an image in a 5 nm waveband centred at 719 nm. This result compared favourably with existing research given the relative simplicity of the segmentation algorithm and complexity of the test images used.

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