Multiple Instance Choquet Integral with Binary Fuzzy Measures for Remote Sensing Classifier Fusion with Imprecise Labels

Classifier fusion methods integrate complementary information from multiple classifiers or detectors and can aid remote sensing applications such as target detection and hy-perspectral image analysis. The Choquet integral (CI), param-eterized by fuzzy measures (FMs), has been widely used in the literature as an effective non-linear fusion framework. Standard supervised CI fusion algorithms often require precise ground-truth labels for each training data point, which can be difficult or impossible to obtain for remote sensing data. Previously, we proposed a Multiple Instance Choquet Integral (MICI) classifier fusion approach to address such label uncertainty, yet it can be slow to train due to large search space for FM variables. In this paper, we propose a new efficient learning scheme using binary fuzzy measures (BFMs) with the MICI framework for two-class classifier fusion given ambiguously and imprecisely labeled training data. We present experimental results on both synthetic data and real target detection problems and show that the proposed MICI-BFM algorithm can effectively and efficiently perform classifier fusion given remote sensing data with imprecise labels.