  The spatial attention is a straightforward approach to enhance the
performance for remote sensing image captioning. However, conventional spatial
attention approaches consider only the attention distribution on one fixed
coarse grid, resulting in the semantics of tiny objects can be easily ignored
or disturbed during the visual feature extraction. Worse still, the fixed
semantic level of conventional spatial attention limits the image understanding
in different levels and perspectives, which is critical for tackling the huge
diversity in remote sensing images. To address these issues, we propose a
remote sensing image caption generator with instance-awareness and
cross-hierarchy attention. 1) The instances awareness is achieved by
introducing a multi-level feature architecture that contains the visual
information of multi-level instance-possible regions and their surroundings. 2)
Moreover, based on this multi-level feature extraction, a cross-hierarchy
attention mechanism is proposed to prompt the decoder to dynamically focus on
different semantic hierarchies and instances at each time step. The
experimental results on public datasets demonstrate the superiority of proposed
approach over existing methods.
