Multi-modal sensor data fusion takes advantage of complementary or reinforcing information from each sensor and can boost overall performance in applications such as scene classification and target detection. This paper presents a new method for fusing multi-modal and multi-resolution remote sensor data without requiring pixel-level training labels, which can be difficult to obtain. Previously, we developed a Multiple Instance Multi-Resolution Fusion (MIMRF) framework that addresses label uncertainty for fusion, but it can be slow to train due to the large search space for the fuzzy measures used to integrate sensor data sources. We propose a new method based on binary fuzzy measures, which reduces the search space and significantly improves the efficiency of the MIMRF framework. We present experimental results on synthetic data and a real-world remote sensing detection task and show that the proposed MIMRF-BFM algorithm can effectively and efficiently perform multi-resolution fusion given remote sensing data with uncertainty.

翻译：多模态传感器数据融合利用各传感器的互补或增强信息，可在场景分类与目标检测等应用中提升整体性能。本文提出一种新方法，用于融合多模态与多分辨率遥感传感器数据，且无需难以获取的像素级训练标签。我们先前开发了解决融合标签不确定性的多示例多分辨率融合（MIMRF）框架，但该框架因整合传感器数据源时模糊测度的搜索空间过大而导致训练缓慢。现提出基于二元模糊测度的新方法，通过缩减搜索空间显著提升MIMRF框架的效率。我们在合成数据与真实遥感检测任务上的实验结果表明：所提出的MIMRF-BFM算法能有效且高效地处理具有不确定性的遥感数据多分辨率融合问题。