Object-Based Postclassification Relearning

Abstract

In this letter, we present an object-based postclassification relearning approach for enhanced supervised remote sensing image classification. Conventional postclassification processing techniques aim to enhance the classification accuracy by imposing smoothness priors in the image domain (based on, for example, majority filtering or Markov random fields). In contrast to that, here, a supervised classification model is learned for the second time, with additional information generated from the initial classification outcome to enhance the discriminative properties of relearned decision functions. This idea is followed within an object-based image analysis framework. Therefore, we model spatial-hierarchical context relations with the preliminary classification outcome by computing class-related features using a triplet of hierarchical segmentation levels. Those features are used to enlarge the initial feature space and impose spatial regularization in the relearned model. We evaluate the relevance of the method in the context of classifying of a high-resolution multispectral image, which was acquired over an urban environment. The experimental results show an enhanced classification accuracy using this method compared to both per-pixel-based approach and outcomes obtained with a conventional object-based postclassification processing technique (i.e., object-based voting).