Efficient Multi-Resolution Fusion for Remote Sensing Data with Label
  Uncertainty
Hersh Vakharia Xiaoxiao Du
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.