  In remote sensing, it is often challenging to acquire or collect a large
dataset that is accurately labeled. This difficulty is usually due to several
issues, including but not limited to the study site's spatial area and
accessibility, errors in the global positioning system (GPS), and mixed pixels
caused by an image's spatial resolution. We propose an approach, with two
variations, that estimates multiple target signatures from training samples
with imprecise labels: Multi-Target Multiple Instance Adaptive Cosine Estimator
(Multi-Target MI-ACE) and Multi-Target Multiple Instance Spectral Match Filter
(Multi-Target MI-SMF). The proposed methods address the problems above by
directly considering the multiple-instance, imprecisely labeled dataset. They
learn a dictionary of target signatures that optimizes detection against a
background using the Adaptive Cosine Estimator (ACE) and Spectral Match Filter
(SMF). Experiments were conducted to test the proposed algorithms using a
simulated hyperspectral dataset, the MUUFL Gulfport hyperspectral dataset
collected over the University of Southern Mississippi-Gulfpark Campus, and the
AVIRIS hyperspectral dataset collected over Santa Barbara County, California.
Both simulated and real hyperspectral target detection experiments show the
proposed algorithms are effective at learning target signatures and performing
target detection.
