This chapter introduces what we call patch-based classification for land cover mapping. This task consists of classifying image patches of fixed size, and attributing the estimated class to the central pixel of the patch to produce a land cover map. This kind of approach is suitable when the available data is sparsely annotated: the only requirement is the location of patches of which the class is known. Typically, examples of terrain truth allowing this approach can be GPS coordinates associated with land cover class, or manually annotated polygons in GIS, etc. This approach involves deep networks that input a patch of image, and produce a single value that represents the class of this patch: hence training this kind of network ...
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