  In remote sensing, each sensor can provide complementary or reinforcing
information. It is valuable to fuse outputs from multiple sensors to boost
overall performance. Previous supervised fusion methods often require accurate
labels for each pixel in the training data. However, in many remote sensing
applications, pixel-level labels are difficult or infeasible to obtain. In
addition, outputs from multiple sensors often have different resolution or
modalities. For example, rasterized hyperspectral imagery presents data in a
pixel grid while airborne Light Detection and Ranging (LiDAR) generates dense
three-dimensional (3D) point clouds. It is often difficult to directly fuse
such multi-modal, multi-resolution data. To address these challenges, we
present a novel Multiple Instance Multi-Resolution Fusion (MIMRF) framework
that can fuse multi-resolution and multi-modal sensor outputs while learning
from automatically-generated, imprecisely-labeled data. Experiments were
conducted on the MUUFL Gulfport hyperspectral and LiDAR data set and a
remotely-sensed soybean and weed data set. Results show improved, consistent
performance on scene understanding and agricultural applications when compared
to traditional fusion methods.
