Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

Artificial Neural Networks in the Assessment of Stand Parameters from an IKONOS Satellite Image (CROSBI ID 147767)

Prilog u časopisu | prethodno priopćenje

Klobučar, Damir ; Pernar, Renata ; Lončarić, Sven ; Subašić, Marko Artificial Neural Networks in the Assessment of Stand Parameters from an IKONOS Satellite Image // Croatian journal of forest engineering, 29 (2008), 2; 201-211

Podaci o odgovornosti

Klobučar, Damir ; Pernar, Renata ; Lončarić, Sven ; Subašić, Marko

engleski

Artificial Neural Networks in the Assessment of Stand Parameters from an IKONOS Satellite Image

The paper explores the possibilities of assessing five stand parameters (tree number, volume, stocking, basal area and stand age) with the application of a multi-layer perceptron artificial neural network. An IKONOS satellite image (PAN 1 m x 1 m) was used to asses parts of stands in the sixth (121– 140 yrs) and seventh (141– 160 yrs) age class of pedunculate oak management class in the &raquo ; Slavir&laquo ; Management Unit of Otok Forest Office. Six features extracted from the first order histogram and five texture features extracted from the second order histogram were used as input data for neural network training. Data from the Management Plan were used as outputs of the neural network. An early stopping method and scaled conjugate gradient algorithm with error back propagation were used to improve generalization property of the neural network. Two neural network models were applied to assess the required stand parameters. The first model has one neuron in the output layer, where separate neuron network training was conducted for each stand parameter. The second model has five neurons in the output layer related to five assessed stand parameters. Both networks were trained and tested simultaneously. The conducted research showed that both of these neuron network models have good generalization properties. However, further analysis gave precedence to the second neural network model. Assessment of five quantitative stand parameters did not show any statistically significant differences between the Management Plan data and the neuron network model in terms of tree number, volume, stocking, basal area and stand age analysis.

rtificial neuron networks; IKONOS – 2; stand parameter assessment; texture

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

nije evidentirano

Podaci o izdanju

29 (2)

2008.

201-211

objavljeno

1845-5719

Povezanost rada

Elektrotehnika, Računarstvo, Šumarstvo

Poveznice
Indeksiranost