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Per-Pixel Water Detection on Surfaces with Unknown Reflectance

Chao Wang,Michihiko Okuyama,Ryo Matsuoka,Takahiro Okabe

Published 2021 in IEICE Trans. Inf. Syst.

ABSTRACT

SUMMARY Water detection is important for machine vision applications such as visual inspection and robot motion planning. In this paper, we propose an approach to per-pixel water detection on unknown surfaces with a hyperspectral image. Our proposed method is based on the water spectral characteristics: water is transparent for visible light but translucent / opaque for near-infrared light and therefore the apparent near-infrared spectral re-flectance of a surface is smaller than the original one when water is present on it. Specifically, we use a linear combination of a small number of basis vector to approximate the spectral reflectance and estimate the original near-infrared reflectance from the visible reflectance (which does not depend on the presence or absence of water) to detect water. We conducted a number of experiments using real images and show that our method, which estimates near-infrared spectral reflectance based on the visible spectral re-flectance, has better performance than existing techniques.

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