In-scene material detection for real-time autonomous hyperspectral reflectance correction

Randall A. Pietersen; Brian M. Robinson; Robert A. Diltz; Herbert H. Einstein

doi:10.1117/12.3013111

7 June 2024 In-scene material detection for real-time autonomous hyperspectral reflectance correction

Randall A. Pietersen, Brian M. Robinson, Robert A. Diltz, Herbert H. Einstein

Proceedings Volume 13030, Image Sensing Technologies: Materials, Devices, Systems, and Applications XI; 1303008 (2024) https://doi.org/10.1117/12.3013111
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

If a U.S. Air Force operated airfield is attacked, the current methodology for assessing its condition is a slow manual inspection process, exposing personnel to dangerous conditions. Advances in drone technology, remote sensing, deep learning, and computer vision have sparked interest in developing autonomous remote solutions. While digital image processing techniques have matured in recent decades, a lack of application-specific training data presents significant obstacles for developing reliable solutions to detect specific objects amongst rubble, debris, variations in pavement types, changing surface features, and other variable runway conditions. Consequently, near-surface hyperspectral imaging has been proposed as an alternative to RGB digital images, due to its discriminatory power in classifying materials. Spatio-spectral data acquired by hyperspectral imagers help address common challenges presented by data scarcity and scene complexity; however, raw data acquired by hyperspectral sensors must first undergo a reflectance correction process before it can be of use. This paper presents an expedient method, tailored to airfield damage assessment, for performing autonomous reflectance correction on near-surface hyperspectral data using in-scene pavement materials with a known spectral reflectance. Unlike most reflectance correction methods, this process eliminates the need for human intervention with the sensor (or its data) pre or post flight and does not require pre-staged reference targets or an additional downwelling irradiance sensor. Positive initial results from real-world flights over pavements are presented and compared to traditional methods of reflectance correction. Three separate flight tests report mean errors between 2% and 2.5% using the new method.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Randall A. Pietersen, Brian M. Robinson, Robert A. Diltz, and Herbert H. Einstein "In-scene material detection for real-time autonomous hyperspectral reflectance correction", Proc. SPIE 13030, Image Sensing Technologies: Materials, Devices, Systems, and Applications XI, 1303008 (7 June 2024); https://doi.org/10.1117/12.3013111

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