Over the past few decades,the research into and application of hyperspectral images has made significant progress,with interest levels progressing from very little to intense.Among the many aspects of this research,hyperspectral image classification has become one of the most-studied topics,with experiments showing that the spatial spectral joint classification method can achieve better classification results than the pixel-by-pixel classification method that relies on spectral information alone.Here,we classified and analyzed a number of spatial spectral joint classification methods.First,we introduced two kinds of spatial dependence relations between adjacent pixels in hyperspectral images;using these,we were able to classify existing spatial-spectral classification methods into fixed-dependent neighborhood-based and adaptive neighborhood-based types.In addition,we were able to further divide existing methods into two types,single-dependency and double-dependency,based on whether or not we used two types of dependencies at the same time.We were also able to divide existing classification methods into three types:preprocessing,integration,and post-processing,according to the different fusion stages of the spatial and spectral information.Finally,we showed the classification results achieved from the application of several representative spatial-spectral classification methods to real hyperspectral datasets.