Watershed segmentation and classification of tree species using high resolution forest imagery

This paper proposes a procedure for classifying tree species in high spatial resolution aerial imagery. In this study, the images Mere observed by video camera mounted on a helicopter. The spatial resolution of these images is about from 7 cm to 10 cm. Since this resolution is higher than one of satellite, tree species can be recognized in details. Tree species for classification are three classes. One class is a broad-leaved tree, and other classes are needle-leaved trees. Each class have different spatial patterns of gray-level and spectral signatures. Although they are the effective features, the various size and shape of tree and shadow make complicated and randomly textured composition in the aerial images. For this reason, we performed a segmentation before classification. The segmentation method is based on watershed algorithm using a gradient of brightness effectively. In classification, the features were extracted from each segmented region. We used gray level co-occurrence matrix as the textural feature and two kinds of spectral features. Supervised classification using maximum likelihood decision rules was performed. We achieved in about 80 to 90 percent of accuracy