  Labeling semantic segmentation datasets is a costly and laborious process if
compared with tasks like image classification and object detection. This is
especially true for remote sensing applications that not only work with
extremely high spatial resolution data but also commonly require the knowledge
of experts of the area to perform the manual labeling. Data augmentation
techniques help to improve deep learning models under the circumstance of few
and imbalanced labeled samples. In this work, we propose a novel data
augmentation method focused on exploring the spatial context of remote sensing
semantic segmentation. This method, ChessMix, creates new synthetic images from
the existing training set by mixing transformed mini-patches across the dataset
in a chessboard-like grid. ChessMix prioritizes patches with more examples of
the rarest classes to alleviate the imbalance problems. The results in three
diverse well-known remote sensing datasets show that this is a promising
approach that helps to improve the networks' performance, working especially
well in datasets with few available data. The results also show that ChessMix
is capable of improving the segmentation of objects with few labeled pixels
when compared to the most common data augmentation methods widely used.
