Estimation of stands parameters from IKONOS satellite images using textural features

We present our research on artificial neural network application in remote sensing analysis of forest management data. The presented research is part of our ongoing investigation of texture analysis application on estimation of stand parameters for the forestry needs. In our investigation we have used IKONOS (PAN 1m × 1m) satellite image. We have used two groups of texture features. The first group is based on first and second order histograms and the second group is based on Fourier transform. We have experimented separately with each feature set and also with both of them combined. We tried radial basis neural networks and multilayer perceptrons with different sets of parameters. Optimal network parameters were calculated and we report results of those optimal neural networks. The stand parameters we were estimating include number of trees, stocking, basal area and volume. Each of the parameters is estimated with its own neural network. Separate estimations are done for VI (121 - 140 yrs) and VII (141 - 160 yrs) age class. The experiments have confirmed good estimation accuracy and good correlation with target values.