This paper introduces Sill-Rgan, a novel Generative Adversarial Network (GAN) designed to improve hyperspectral image (HSI) classification under varying lighting conditions. Sill-Rgan uniquely maps different light condition domains, enhancing sample classification robustness and generating new virtual samples. Addressing challenges like high spectral dimensionality and noise in HSI classification, our approach utilizes a deep proxy-based learning framework. It integrates and improves advanced GAN models and multitask networks for optimal training stability and loss function optimization. The model's mapping network is adept at generating domain-specific latent codes, enabling the transformation of original hyperspectral data into enhanced versions. Extensive experiments conducted on hyperspectral datasets of agricultural products under diverse indoor and outdoor lighting conditions confirm the effectiveness of Sill-Rgan. The results highlight the model's adaptability in both supervised and semi-supervised learning scenarios, yielding exceptional classification accuracy and enhanced data quality. The versatile potential of Sill-Rgan extends its applicability to a broad range of spectral data classifications, underlining its significant contribution to hyperspectral imaging. This advancement opens new avenues in machine vision systems, particularly in scenarios with dynamic lighting challenges.

中文翻译：

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使用生成对抗网络的鲁棒高光谱图像分类

本文介绍了 Sill-Rgan，一种新颖的生成对抗网络 (GAN)，旨在改进不同照明条件下的高光谱图像 (HSI) 分类。 Sil-Rgan 独特地映射了不同的光照条件域，增强了样本分类的稳健性并生成新的虚拟样本。为了解决 HSI 分类中的高光谱维数和噪声等挑战，我们的方法利用基于深度代理的学习框架。它集成并改进了先进的 GAN 模型和多任务网络，以实现最佳的训练稳定性和损失函数优化。该模型的映射网络擅长生成特定领域的潜在代码，从而能够将原始高光谱数据转换为增强版本。在不同的室内和室外照明条件下对农产品的高光谱数据集进行的广泛实验证实了 Sill-Rgan 的有效性。结果突显了该模型在监督和半监督学习场景中的适应性，从而产生了出色的分类准确性并提高了数据质量。 Sil-Rgan 的多功能潜力将其适用性扩展到广泛的光谱数据分类，突显了其对高光谱成像的重大贡献。这一进步为机器视觉系统开辟了新的途径，特别是在面临动态照明挑战的场景中。