Dr. Alessandro Nicolosi Profile

Dr. Alessandro Nicolosi

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Proceedings Article | 19 October 2023 Presentation + Paper

Improve target detection on hyperspectral aerial images using simulated atmosphere conditions

Rosario Di Carlo, Roberto Morelli, Alessandro Nicolosi

Proceedings Volume 12733, 127330J (2023) https://doi.org/10.1117/12.2677985

KEYWORDS: Deep learning, Atmospheric modeling, Data modeling, Atmospheric sensing, Target detection, 3D modeling, Atmospheric physics, Hyperspectral imaging, Sensors

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In the context of remote sensing classification, the performance of data-driven models in identifying ground objects is influenced by the variability of conditions during the acquisition process. This includes atmospheric conditions which strongly influence the radiance value collected by the hyperspectral camera and can hinder the generalization performance of the algorithms. Moreover, due to the difficulty of obtaining pixel-level annotations, hyperspectral models are typically trained on a limited quantity of data. Although these models may perform well on small validation datasets, their performance may not be adequate for real-world applications of hyperspectral imagery, which entail a wide range of conditions. This paper proposes an augmentation strategy to increase the diversity of data and enhance the robustness of the model under different atmospheric conditions. To achieve this, a physics-based radiative transfer model is used to first correct the atmospheric effects and then simulate new data under different atmospheric conditions. This step increases the diversity of the data by generating a wide range of conditions that the models may encounter in real-world applications. The method employs a 3D convolution-based model that extracts both spatial and spectral features for small houses detection on a proprietary dataset. The study results demonstrate the efficacy of the proposed method, as the augmented model outperforms the baseline model in terms of F1 score on the augmented test images and shows comparable performances on the original scenario.

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