  Semantic segmentation in very high resolution (VHR) aerial images is one of
the most challenging tasks in remote sensing image understanding. Most of the
current approaches are based on deep convolutional neural networks (DCNNs).
However, standard convolution with local receptive fields fails in modeling
global dependencies. Prior researches have indicated that attention-based
methods can capture long-range dependencies and further reconstruct the feature
maps for better representation. Nevertheless, limited by the mere perspective
of spacial and channel attention and huge computation complexity of
self-attention mechanism, it is unlikely to model the effective semantic
interdependencies between each pixel-pair of remote sensing data of complex
spectra. In this work, we propose a novel attention-based framework named
Hybrid Multiple Attention Network (HMANet) to adaptively capture global
correlations from the perspective of space, channel and category in a more
effective and efficient manner. Concretely, a class augmented attention (CAA)
module embedded with a class channel attention (CCA) module can be used to
compute category-based correlation and recalibrate the class-level information.
Additionally, we introduce a simple yet effective region shuffle attention
(RSA) module to reduce feature redundant and improve the efficiency of
self-attention mechanism via region-wise representations. Extensive
experimental results on the ISPRS Vaihingen and Potsdam benchmark demonstrate
the effectiveness and efficiency of our HMANet over other state-of-the-art
methods.
