A Feature Selection Approach for Segmentation of Very High-Resolution Satellite Images

Abstract

Most of the feature selection (fs) methods in the literature determine features that are appropriate only for a given dataset. In contrast, in this paper a FS method that is not dependent to a specific dataset is proposed. In this regard, the effective feature types based on reasonable facts are predefined and appropriate candidate features for each feature type are selected. In proposed method, the features selected from a single labeled image can be used in segmentation of images captured by different satellites with similar spatial resolution. The selected feature types contain spatial and spectral features. The selected features are applied for segmentation of the images captured by QuickBird and GeoEye satellites and obtained results of proposed method are compared with well-known FS methods. Using different evaluation measures, our comparison shows the efficiency of the proposed method in providing better segmentation compared to other FS methods that are presented in this paper.