  Semantic segmentation of remote sensing images plays an important role in a
wide range of applications including land resource management, biosphere
monitoring and urban planning. Although the accuracy of semantic segmentation
in remote sensing images has been increased significantly by deep convolutional
neural networks, several limitations exist in standard models. First, for
encoder-decoder architectures such as U-Net, the utilization of multi-scale
features causes the underuse of information, where low-level features and
high-level features are concatenated directly without any refinement. Second,
long-range dependencies of feature maps are insufficiently explored, resulting
in sub-optimal feature representations associated with each semantic class.
Third, even though the dot-product attention mechanism has been introduced and
utilized in semantic segmentation to model long-range dependencies, the large
time and space demands of attention impede the actual usage of attention in
application scenarios with large-scale input. This paper proposed a
Multi-Attention-Network (MANet) to address these issues by extracting
contextual dependencies through multiple efficient attention modules. A novel
attention mechanism of kernel attention with linear complexity is proposed to
alleviate the large computational demand in attention. Based on kernel
attention and channel attention, we integrate local feature maps extracted by
ResNeXt-101 with their corresponding global dependencies and reweight
interdependent channel maps adaptively. Numerical experiments on three
large-scale fine resolution remote sensing images captured by different
satellite sensors demonstrate the superior performance of the proposed MANet,
outperforming the DeepLab V3+, PSPNet, FastFCN, DANet, OCRNet, and other
benchmark approaches.
