Feature Selection Methods for Object-based Classification of Sub-decimeter Resolution Digital Aerial Imagery

The availability of numerous spectral, spatial, and contextual features renders the selection of optimal features a time consuming and subjective process in object-based image analysis (OBIA). While several feature selection methods have been used in conjunction with OBIA, a robust comparison of the utility and efficiency of approaches could facilitate broader application. In this study, we tested three feature selection methods, 1) Jeffreys-Matusita distance (JM), 2) classification tree analysis (CTA), and 3) feature space optimization (FSO) for object-based classifications of rangeland vegetation with sub-decimeter digital aerial imagery in the arid southwestern U.S. We assessed strengths, weaknesses, and best uses for each approach using the criteria of ease of use, ability to rank and/or reduce input features, and classification accuracies. For the five sites tested, JM resulted in the highest overall classification accuracies for three sites, while CTA was highest for two sites. FSO resulted in the lowest accuracies. CTA offered ease of use and ability to rank and reduce features, while JM had the advantage of assessing class separation distances. FSO allowed for determining features relatively quickly, because it operates within the eCognition software used in this analysis. However, the feature ranking in FSO is unclear and accuracies were relatively low. While all methods offered an objective approach for determining suitable features for classifications of sub-decimeter resolution aerial imagery, we concluded that CTA was best suited for this particular dataset. We explore the limitations, assumptions, and appropriate uses for this and other datasets.