Object-based feature selection for crop classification using multi-temporal high-resolution imagery

ABSTRACTWith high-resolution remote-sensing data, there are numerous possible features for object description, making the selection of optimal features a time-consuming and subjective process. While substantial efforts have been made to compare the utility of feature selection metrics, less attention has been paid to the efficiency of such in the context of object-based image analysis. In this study, the statistical measurement z-score was used to ensure compatibility with objects. We assessed the feasibility of a z-score method, and then ranked and reduced input features using a backward elimination technique. The results showed that separability can be efficiently estimated based on z-score values, and the near-infrared band performed the best for crop classification. A straightforward trend was observed, and the optimal feature set was created, which was a combination of spectral, temporal, texture information and vegetation indices. These features complement one another to help increase crop map accur...