Separation of coniferous species in boreal forest using spectral and contextual features from ikonos imagery

Trees were located and classified to pine and spruce classes using features computed from Ikonos multispectral and panchromatic channels at study site in Eastern Finland. Spectral signatures were sampled from the extracted tree locations, and a set of contextual features was computed in the neighborhood around each located tree from the Ikonos panchromatic channel. Circular masks of five different sizes were used. The contextual features included higher order statistical features (skewness, kurtosis), and additional features obtained by fitting either two Gaussian distributions or a Weibull distribution to the intensity histogram. The contextual features aimed at capturing differences in the distribution of intensities of pine and spruce crowns. Pure (100%) pine and spruce plots with medium stem volume (100 - 200 m³/ha), and with a minimum distance of 15 m from stand borders, were used in the study. The training data contained 5 plots of pine and 5 plots of spruce, from which 196 trees were located (96 pine, 100 spruce). The separate validation data set consisted of 6 plots (3 plots both pine and spruce) containing 116 trees (54 pine, 62 spruce). Stepwise linear discriminant analysis was used to select the best separating features and for classification. From the multispectral channels, the best separating feature was the blue channel. From the contextual features the best separating features were the Weibull shape parameter, the ratio of sample mean and median, kurtosis, and skewness, the set of best features being slightly different for different sampling radii. For the validation data set, the percentage of correctly classified trees was 87.9% when using spectral channels only, and increased from 81.9% to 98.3% along with increasing sampling radius using only the contextual features. The classification accuracy reached 99.1% when both spectral and contextual features were used.