  In this work we consider the application of convolutional neural networks
(CNNs) for pixel-wise labeling (a.k.a., semantic segmentation) of remote
sensing imagery (e.g., aerial color or hyperspectral imagery). Remote sensing
imagery is usually stored in the form of very large images, referred to as
"tiles", which are too large to be segmented directly using most CNNs and their
associated hardware. As a result, during label inference, smaller sub-images,
called "patches", are processed individually and then "stitched" (concatenated)
back together to create a tile-sized label map. This approach suffers from
computational ineffiency and can result in discontinuities at output
boundaries. We propose a simple alternative approach in which the input size of
the CNN is dramatically increased only during label inference. This does not
avoid stitching altogether, but substantially mitigates its limitations. We
evaluate the performance of the proposed approach against a vonventional
stitching approach using two popular segmentation CNN models and two
large-scale remote sensing imagery datasets. The results suggest that the
proposed approach substantially reduces label inference time, while also
yielding modest overall label accuracy increases. This approach contributed to
our wining entry (overall performance) in the INRIA building labeling
competition.
