  Many significant applications need land cover information of remote sensing
images that are acquired from different areas and times, such as change
detection and disaster monitoring. However, it is difficult to find a generic
land cover classification scheme for different remote sensing images due to the
spectral shift caused by diverse acquisition condition. In this paper, we
develop a novel land cover classification method that can deal with large-scale
data captured from widely distributed areas and different times. Additionally,
we establish a large-scale land cover classification dataset consisting of 150
Gaofen-2 imageries as data support for model training and performance
evaluation. Our experiments achieve outstanding classification accuracy
compared with traditional methods.
