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A multilevel decision fusion approach for urban mapping using very high-resolution multi/hyperspectral imagery

A novel multilevel decision fusion approach is proposed for urban mapping using very-high-resolution (VHR) multi/hyperspectral imagery. The proposed framework consists of three levels: (1) at level I, we first propose a self-dual filter for extracting structural features from the VHR imagery–subsequently, the spectral and structural features are integrated based on a weighted probability fusion; (2) level II extends level I by implementing the spectral–structural fusion in an object-based framework; and (3) at level III, the object-based probabilistic outputs at level II are used to identify unreliable objects, and shape attributes of these unreliable objects are then considered for refinement of classification. At this level, a decision-level object merging is used to improve the initial segmentation, since shape feature extraction is highly dependent on the quality of segmentation. Experiments were conducted on a Hyperspectral Digital Imagery Collection Experiment (HYDICE) DC Mall image and a QuickBird Beijing data set. The results revealed that the proposed approach provided progressively increasing accuracies when the multilevel features were gradually considered in the processing chain.