The study of flood detection is significant to human life and social economy. In this paper, a completely unsupervised flood detection approach is presented, which combines spatio-temporal context and histogram thresholding. A global thresholding algorithm can be used in most of the cases to distinguish flood from non-flood pixels, but it may not distinguish local grey-level changes when the method is unsupervised. In this work, we introduce a kind of local context information to improve the results. A statistical model is used to establish the spatial relationships between each pixel and its surrounding regions, then a confidence map is computed. If the context structure changes significantly, the pixel is then considered potentially abnormal. Experimental investigations performed on HJ-1B CCD data from Northeast China during large-scale flooding in August 2013 showed higher precision of the proposed approach.