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Multitarget Multiple-Instance Learning for Hyperspectral Target Detection
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DOI：10.1109/TGRS.2021.3060966.png)
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Abstract
En 中文
In remote sensing, it is often challenging to acquire or collect a large data set that is accurately labeled. This difficulty is usually due to several issues, including but not limited to the study sitex2019;s spatial area and accessibility, errors in the global positioning system (GPS), and mixed pixels caused by an imagex2019;s spatial resolution. We propose an approach, with two variations, that estimates multiple-target signatures from training samples with imprecise labels: multitarget multiple-instance adaptive cosine estimator (MTMI-ACE) and multitarget multiple-instance spectral match filter (MTMI-SMF). The proposed methods address the abovementioned problems by directly considering the multiple-instance, imprecisely labeled data set. 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 data set, the MUUFL Gulfport hyperspectral data set collected over the University of Southern Mississippix2013;Gulfpark Campus, and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data set collected over Santa Barbara County, CA, USA. Both simulated and real hyperspectral target detection experiments show that the proposed algorithms are effective at learning target signatures and performing target detection.
Keywords:
Hyperspectral imaging
Object detection
Training
Training data
Dictionaries
Libraries
Global Positioning System
Adaptive cosine estimator (ACE)
hyperspectral
multiple instance
multiple target
spectral matched filter
target characterization
target detection
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