Constructing Hierarchical Segmentation Tree for Feature Extraction and Land Cover Classification of High Resolution MS Imagery

Accurate interpretation of high spatial resolution multispectral (MS) imagery relies on the extraction and fusion of information obtained from both spectral and spatial domains. Feature extraction from one or several fixed windows uses inaccurate description of pixel contexts and produces blurred object boundaries and low classification accuracy. In order to accurately characterize the spatial context properties of pixels, this paper presents a hierarchical-segmentation-based classification system. The system consists of two main modules: 1) hierarchical segmentation and 2) context-based classification. The segmentation module involves an optimization procedure to prevent under-segmentation of the land objects of interest and a scale selection procedure to find the most representative segmentation layers for modeling pixel contexts. The classification module couples a context-driven multilevel feature extraction methodology with a support vector machine classifier to get classification result. The proposed system is validated on three high spatial resolution MS data sets. Compared with state-of-the-art classification methods based on the similar concept, the proposed method demonstrates superior performance on both the classification accuracy and the quality of classification maps.