Spectral and Texture Features Combined for Forest Tree species Classification with Airborne Hyperspectral Imagery

Forest precision classification products were the basic data for surveying of forest resource, updating forest subplot information, logging and management of forest. However, due to the diversity of stand structure, complexity of the forest growth environment, it is difficult to discriminate forest tree species using multi-spectral image. The airborne hyper-spectral images can obtain high spatial and spectral resolution imagery of forest canopy, so it may be useful for tree species level classification. The aim of this paper was to test the effective of combining spatial and spectral features in airborne hyper-spectral image classification. The CASI hyper spectral image data were acquired from Liangshui natural reserves area. First the MNF (minimum noise fraction) transform method for to reduce the hyperspectral image dimensionality and highlighting variation. Second, the grey level co-occurrence matrix (GLCM) is used to extract the texture features of forest tree canopy. Thirdly the texture and the spectral features of forest canopy were fused to classify the trees species using support vector machine (SVM) with different kernel functions. The results showed that when using the SVM classifier, MNF and texture-based features combined with linear kernel function can achieve the best overall accuracy which was 85.92 %. It also confirmed the belief that combined the spatial and spectral information can improve the accuracy of tree species classification.