Abstract:

In many remote sensing and hyperspectral image analysis applications, precise ground truth information is unavailable or impossible to obtain. Imprecision in ground truth often results from highly mixed or sub-pixel spectral responses over classes of interest, a mismatch between the precision of global positioning system (GPS) units and the spatial resolution of collected imagery, and misalignment between multiple sources of data. Given these sorts of imprecision, training of traditional supervised machine learning models which rely on the assumption of accurate and precise ground truth becomes intractable. Multiple instance learning (MIL) is a methodology that can be used to address these challenging problems. This chapter investigates the topic of hyperspectral image analysis given imprecisely labeled data and reviews MIL methods for hyperspectral target detection, classification, data fusion, and regression.

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Citation:

@Inbook{Jiao2020,
author="Jiao, Changzhe and Du, Xiaoxiao and Zare, Alina",
editor="Prasad, Saurabh and Chanussot, Jocelyn",
title="Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis",
bookTitle="Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="141--185",
isbn="978-3-030-38617-7",
doi="10.1007/978-3-030-38617-7_6",
url="https://doi.org/10.1007/978-3-030-38617-7_6"
}