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

With 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 accuracy. For the 40% of the entire sample sizes, the optimal feature sets produced the best trade-off between the number of inputs and classification accuracy, with the misclassification error of 7.09%. Additionally, reliable crop maps were obtained, with the overall accuracy of 92.64%, and the z-score method showed great potential for the separability of crops at object scale using remotely sensed multi-temporal data. [ABSTRACT FROM AUTHOR]