Fusion of spectral and spatial features for human settlement extraction

The characterization of urban areas can be improved considerably by combining spectral and spatial features. As a matter of fact, depending on objects of interest in a specific application, the exploitation of both types of features at multiple spatial resolutions is required. This paper proposes a decision fusion method that relies on both spectral and textural features. The proposed approach is able to produce different classification results based on distinct partitions of the same input data set. Experiments conducted on CBERS-2B data demonstrate a significant performance improvement brought by the combination of spectral and textural features in comparison to the use of only spectral features to describe the image objects.