A dynamic hierarchical feature selection method for object-based classification of wetlands
Wetland classification has always been a challenging task among remote sensing experts. Typically, wetland classes have low accuracies regardless of the applied dataset, as they have many spectral and ecological similarities. In this paper, a method is developed particularly effective for distinguishing spectrally similar classes such as wetlands. In this method, feature selection and object-based classification are not done in one step, but instead several feature selections and classifications are applied, and in each level a target class is classified and masked out. While classifying the target class, other spectrally resembling classes are merged so that feature selection is mainly concentrated on separating two classes only. Object-based features were extracted from several SAR and optical images, including RADARSAT-2, ALOS-1, ALOS-2, RapidEye and Landsat-8 images. 15 and 10 percent improvement was obtained in wetlands' average producer and user accuracies compared to the typical feature selection by using the proposed method.