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10 November 2004 Physical subspace models for invariant material identification: subspace composition and detection performance
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Abstract
We study material identification in a forest scene under strongly varying illumination conditions, ranging from open sunlit conditions to shaded conditions between dense tree-lines. The algorithm used is a physical subspace model, where the pixel spectrum is modelled by a subspace of physically predicted radiance spectra. We show that a pure sunlight and skylight model is not sufficient to detect shaded targets. However, by expanding the model to also represent reflected light from the surrounding vegetation, the performance of the algorithm is improved significantly. We also show that a model based on a standardized set of simulated conditions gives results equivalent to those obtained from a model based on measured ground truth spectra. Detection performance is characterized as a function of subspace dimensionality, and we find an optimum at around four dimensions. This result is consistent with what is expected from the signal-to-noise ratio in the data set. The imagery used was recorded using a new hyperspectral sensor, the Airborne Spectral Imager (ASI). The present data were obtained using the visible and near-infrared module of ASI, covering the 0.4-1.0 μm region with 160 bands. The spatial resolution is about 0.2 mrad so that the studied targets are resolved into pure pixels.
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Pal Erik Goa, Torbjorn Skauli, Ingebjorg Kasen, Trym Vegard Haavardsholm, and Anders Rodningsby "Physical subspace models for invariant material identification: subspace composition and detection performance", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); https://doi.org/10.1117/12.565612
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