Dear All, I have to develop a classifier to detect olive tree canopies using Geoeye-1 images. I had thought of segmentation and indeed the segmentation is capable of separating and detecting canopies. However I need now a classifier that retains only the segments that represent olive trees. The range of values assigned to canopies is quite high and doesn't follow a particular order, hence i haven't found an obvious mapcalc rule so far. However, I have been reasoning about the particular pattern that characterise olive orchards: olive trees are obviously round, disposed on regular grids, at constant distance to each other, and the canopies, although having different sizes (within a certain range), are almost always well separated from each other. I am now looking to a way to translate this concept into operational rule. I'm sure that this is nothing new, so I was wondering if you could point me relevant literature and existing tools to put this in practice.
Thank you in advance for any hints -- Best regards, Dr. Margherita DI LEO Scientific / technical project officer European Commission - DG JRC Institute for Environment and Sustainability (IES) Via Fermi, 2749 I-21027 Ispra (VA) - Italy - TP 261 Tel. +39 0332 78 3600 [email protected] Disclaimer: The views expressed are purely those of the writer and may not in any circumstance be regarded as stating an official position of the European Commission.
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