AANet: an attention-based alignment semantic segmentation network for high spatial resolution remote sensing images. Issue 13 (3rd July 2022) - Xue, Gunagkuo; Liu, Yikun; Huang, Yuwen; Li, Mingsong; Yang, Gongping
ABSTRACT: In this paper, we present an efficient network to tackle three critical problems in high spatial resolution (HSR) remote sensing image segmentation: ( i ) feature misalignment, ( i i ) insufficient contextual information extraction and ( i i i ) various class imbalance issues. In detail, we propose a novel Feature Alignment Block (FAB) to suppress misalignment issues with the guide of an anchor map. Further, to extract sufficient information, we design a Contextual Augmentation Block (CAB) to augment features of different semantic levels. Finally, we present an Annealing Online Hard Example Mining (AOHEM) strategy to handle the various class imbalance issues with a view to dynamically adjust the focus of the network. We apply the above proposed designs to FPN to form our Attention-based Alignment Network (AANet). Experimental results demonstrate that the proposed method achieves promising results on the challenging iSAID and Vaihingen datasets with a better trade-off between accuracy and complexity.