  While initially devised for image categorization, convolutional neural
networks (CNNs) are being increasingly used for the pixelwise semantic labeling
of images. However, the proper nature of the most common CNN architectures
makes them good at recognizing but poor at localizing objects precisely. This
problem is magnified in the context of aerial and satellite image labeling,
where a spatially fine object outlining is of paramount importance. Different
iterative enhancement algorithms have been presented in the literature to
progressively improve the coarse CNN outputs, seeking to sharpen object
boundaries around real image edges. However, one must carefully design, choose
and tune such algorithms. Instead, our goal is to directly learn the iterative
process itself. For this, we formulate a generic iterative enhancement process
inspired from partial differential equations, and observe that it can be
expressed as a recurrent neural network (RNN). Consequently, we train such a
network from manually labeled data for our enhancement task. In a series of
experiments we show that our RNN effectively learns an iterative process that
significantly improves the quality of satellite image classification maps.
