Comparing object-based and pixel-based classifications for mapping savannas

The development of robust object-based classification methods suitable for medium to high resolution satellite imagery provides a valid alternative to ‘traditional’ pixel-based methods. This paper compares the results of an object-based classification to a supervised per-pixel classification for mapping land cover in the tropical north of the Northern Territory. The object-based approach involved the segmentation of image data into objects at multiple scale levels. Objects were assigned classes using Nearest Neighbour supervised algorithm and fuzzy classification. The supervised pixel-based classification involved the selection of training areas and a classification using the maximum likelihood classifier algorithm. Site-specific accuracy assessment using confusion matrices of both classifications were undertaken based on 256 reference sites. A comparison of the results shows a statistically significant higher overall accuracy of the object-based classification over the pixel-based classification. The incorporation of a digital elevation model (DEM) layer and associated class rules into the object-based classification produced slightly higher accuracies overall and for the associated classes; however this was not statistically significant over the object-based using spectral information solely. The difference in accuracies between the per-pixel method and the object-based method are shown to be statistically significant; indicating object-based analysis has great potential for extracting land cover information from satellite imagery captured over spatially heterogeneous land covers of tropical Australia.