Zaznavanje olesenele vegetacije s konvolucijsko nevronsko mrežo U-Net
DOI:
https://doi.org/10.3986/GV95205Keywords:
nevronska mreža, olesenela vegetacija, ortofoto, model višin krošenj, klasifikacija, //, neural network, woody vegetation, orthophoto, canopy height model, classificationAbstract
S konvolucijsko nevronsko mrežo U-Net smo zaznavali posamezna drevesa, skupine dreves in grmovnic, drevorede, gozd, mejice, obvodno vegetacijo in drevesa v sadovnjakih, ki jih gradijo različne vrste olesenele vegetacije. Osnovni vhodni podatek je bil ortofoto, ki smo mu z namenom boljše klasifikacije dodajali dodatne sloje. Najboljši rezultat smo dobili s kombinacijo ortofota in modela višin krošenj – skupna natančnost klasifikacije je bila 93,3%. Ta kombinacija vhodnih podatkov je zvišala natančnost klasifikacije razredov gozd in ostalo, ki predstavljata daleč največji delež učnih vzorcev, medtem ko je bil rezultat za manj zastopane razrede (zlasti drevored) tudi pri tej klasifikaciji slabši.
Woody vegetation detection with the U-Net convolutional neural network
Single trees, groups of trees and bushes, trees in rows, forest, hedges, riparian vegetation and trees in orchards, all of which are composed by different woody vegetation species, were classified using convolutional neural network U-Net. National orthophoto represented basic input data, however adding further layers was tested with a view of improving classification. Best results were obtained using orthophoto and canopy height model combination, the overall accuracy reached 93.3%. This combination increased accuracies achieved at two biggest classes (classes forest and other). The model performed much worse with other classes, that were less represented in training data, which is especially true for trees in rows.
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