Abstract
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.
Abstract (translated)
多模态传感器数据的融合利用每个传感器互补或增强的信息，可以在诸如场景分类和目标检测等应用中提高整体性能。本文提出了一种不需要像素级训练标签的新方法来融合多模态和多分辨率远程传感器数据，这是难以获得的。之前，我们开发了一种Multiple Instance Multi-Resolution Fusion (MIMRF)框架，用于解决融合时的标签不确定性，但由于使用的模糊度量具有较大的搜索空间，训练可能变得缓慢。我们提出了一种基于二进制模糊度量的全新方法，这减少了搜索空间，显著提高了MIMRF框架的效率。我们在合成数据和现实世界的遥感检测任务上进行了实验，并证明了所提出的MIMRF-BFM算法可以有效且高效地对具有不确定性的遥感数据进行多分辨率融合。
URL
https://arxiv.org/abs/2402.05045
