  Multimodal object detection has shown promise in remote sensing. However,
multimodal data frequently encounter the problem of low-quality, wherein the
modalities lack strict cell-to-cell alignment, leading to mismatch between
different modalities. In this paper, we investigate multimodal object detection
where only one modality contains the target object and the others provide
crucial contextual information. We propose to resolve the alignment problem by
converting the contextual binary information into probability maps. We then
propose an early fusion architecture that we validate with extensive
experiments on the DOTA dataset.
