  Land cover classification of remote sensing images is a challenging task due
to limited amounts of annotated data, highly imbalanced classes, frequent
incorrect pixel-level annotations, and an inherent complexity in the semantic
segmentation task. In this article, we propose a novel architecture called the
dense dilated convolutions' merging network (DDCM-Net) to address this task.
The proposed DDCM-Net consists of dense dilated image convolutions merged with
varying dilation rates. This effectively utilizes rich combinations of dilated
convolutions that enlarge the network's receptive fields with fewer parameters
and features compared with the state-of-the-art approaches in the remote
sensing domain. Importantly, DDCM-Net obtains fused local- and global-context
information, in effect incorporating surrounding discriminative capability for
multiscale and complex-shaped objects with similar color and textures in very
high-resolution aerial imagery. We demonstrate the effectiveness, robustness,
and flexibility of the proposed DDCM-Net on the publicly available ISPRS
Potsdam and Vaihingen data sets, as well as the DeepGlobe land cover data set.
Our single model, trained on three-band Potsdam and Vaihingen data sets,
achieves better accuracy in terms of both mean intersection over union (mIoU)
and F1-score compared with other published models trained with more than
three-band data. We further validate our model on the DeepGlobe data set,
achieving state-of-the-art result 56.2% mIoU with much fewer parameters and at
a lower computational cost compared with related recent work. Code available at
https://github.com/samleoqh/DDCM-Semantic-Segmentation-PyTorch
