报告摘要：In this talk, we will provide a comprehensive overview on the methods belonging to the category of spectral-spatial classification in a relatively unified context. We develop a concept of spatial dependency system that involves pixel dependency and label dependency, with two main factors: neighborhood covering and neighborhood importance. In terms of the way that the neighborhood information is used, the spatial dependency systems can be classified into fixed, adaptive, and global systems, which can accommodate various kinds of existing spectral-spatial methods. Then, we categorize the existing spectral-spatial methods into four paradigms according to the different fusion stages wherein spatial information takes effect, i.e.,preprocessing-based,integrated, postprocessing-based, and hybrid classifications. Typical methodologies are outlined. Finally, experimental results with several representative spectral-spatial classification methods are discussed.