  The trade-off between feature representation power and spatial localization
accuracy is crucial for the dense classification/semantic segmentation of
aerial images. High-level features extracted from the late layers of a neural
network are rich in semantic information, yet have blurred spatial details;
low-level features extracted from the early layers of a network contain more
pixel-level information, but are isolated and noisy. It is therefore difficult
to bridge the gap between high and low-level features due to their difference
in terms of physical information content and spatial distribution. In this
work, we contribute to solve this problem by enhancing the feature
representation in two ways. On the one hand, a patch attention module (PAM) is
proposed to enhance the embedding of context information based on a patch-wise
calculation of local attention. On the other hand, an attention embedding
module (AEM) is proposed to enrich the semantic information of low-level
features by embedding local focus from high-level features. Both of the
proposed modules are light-weight and can be applied to process the extracted
features of convolutional neural networks (CNNs). Experiments show that, by
integrating the proposed modules into the baseline Fully Convolutional Network
(FCN), the resulting local attention network (LANet) greatly improves the
performance over the baseline and outperforms other attention based methods on
two aerial image datasets.
