Vegetation Identification Based on Satellite Imagery

Automatic vegetation identification plays an important role in many applications including remote sensing and high performance flight simulations. This paper presents a method to automatically identify vegetation based upon satellite imagery. First, we utilize the ISODATA algorithm to cluster pixels in the images where the number of clusters is determined by the algorithm. We then apply morphological operations to the clustered images to smooth the boundaries between clusters and to fill holes inside clusters. After that, we compute six features for each cluster. These six features then go through a feature selection algorithm and three of them are determined to be effective for vegetation identification. Finally, we classify the resulting clusters as vegetation and nonvegetation types based on the selected features using a multilayer percetron (MLP) classifier. We tested our algorithm by using the 5-fold cross-validation method and achieved 96% classification accuracy based on the three selected features