  Semantic segmentation requires methods capable of learning high-level
features while dealing with large volume of data. Towards such goal,
Convolutional Networks can learn specific and adaptable features based on the
data. However, these networks are not capable of processing a whole remote
sensing image, given its huge size. To overcome such limitation, the image is
processed using fixed size patches. The definition of the input patch size is
usually performed empirically (evaluating several sizes) or imposed (by network
constraint). Both strategies suffer from drawbacks and could not lead to the
best patch size. To alleviate this problem, several works exploited
multi-context information by combining networks or layers. This process
increases the number of parameters resulting in a more difficult model to
train. In this work, we propose a novel technique to perform semantic
segmentation of remote sensing images that exploits a multi-context paradigm
without increasing the number of parameters while defining, in training time,
the best patch size. The main idea is to train a dilated network with distinct
patch sizes, allowing it to capture multi-context characteristics from
heterogeneous contexts. While processing these varying patches, the network
provides a score for each patch size, helping in the definition of the best
size for the current scenario. A systematic evaluation of the proposed
algorithm is conducted using four high-resolution remote sensing datasets with
very distinct properties. Our results show that the proposed algorithm provides
improvements in pixelwise classification accuracy when compared to
state-of-the-art methods.
