Object-based feature selection using class-pair separability for high-resolution image classification

With the increasing popularity of object-based image analysis (OBIA) since 2006, numerous classification and mapping tasks were reported to benefit from this evolving paradigm. In these studies, segments are firstly created, followed by classification based on segment-level information. However, the feature space formed by segment-level feature variables can be very large and complex, posing challenges to obtaining satisfactory classification performance. Accordingly, this work attempts to develop a new feature selection approach for segment-level features. Based on the principle of class-pair separability, the segment-level features are grouped according to their types. For each group, the contribution of each segment-level feature to the separation of a pair of classes is quantified. With the information of all feature groups and class pairs, the separability ranking and appearance frequency are considered to compute importance score for each feature. Higher importance score means larger appropriateness to select a feature. By using two Gaofen-2 multi-spectral images, the proposed method is validated. The experimental results show the advantages of the proposed technique over some state-of-the-art feature selection approaches: (1) it can better reduce the number of segment-level features and effectively avoid redundant information; (2) the feature subset obtained by the proposed scheme has good potential to improve classification accuracy.