Lee Gwanghyeong, Ghimire Deepak, Kim Donghoon, Cho Sewoon, Kim Byoungjun, Jeong Sunghwan
IT Application Research Center, Korea Electronics Technology Institute, Jeonju 54853, Republic of Korea.
Sensors (Basel). 2025 Jun 30;25(13):4076. doi: 10.3390/s25134076.
Hyperspectral imaging (HSI) is a powerful tool for precision imaging tasks such as vegetation analysis, but its widespread use remains limited due to the high cost of equipment and challenges in data acquisition. To explore a more accessible alternative, we propose a Green Normalized Difference Vegetation Index (GNDVI)-guided green channel adjustment method, termed G-RGB, which enables the estimation of near-infrared (NIR) reflectance from standard RGB image inputs. The G-RGB method enhances the green channel to encode NIR-like information, generating a spectrally enriched representation. Building on this, we introduce FUSE-Net, a novel deep learning model that combines multi-scale convolutional layers and MLP-Mixer-based channel learning to effectively model spatial and spectral dependencies. For evaluation, we constructed a high-resolution RGB-HSI paired dataset by capturing basil leaves under controlled conditions. Through ablation studies and band combination analysis, we assessed the model's ability to recover spectral information. The experimental results showed that the G-RGB input consistently outperformed unmodified RGB across multiple metrics, including mean squared error (MSE), peak signal-to-noise ratio (PSNR), spectral correlation coefficient (SCC), and structural similarity (SSIM), with the best performance observed when paired with FUSE-Net. While our method does not replace true NIR data, it offers a viable approximation during inference when only RGB images are available, supporting cost-effective analysis in scenarios where HSI systems are inaccessible.
高光谱成像（HSI）是用于植被分析等精确成像任务的强大工具，但由于设备成本高和数据采集方面的挑战，其广泛应用仍然有限。为了探索一种更易于使用的替代方法，我们提出了一种由绿色归一化植被指数（GNDVI）引导的绿色通道调整方法，称为G-RGB，它能够从标准RGB图像输入中估计近红外（NIR）反射率。G-RGB方法增强绿色通道以编码类似近红外的信息，生成光谱丰富的表示。在此基础上，我们引入了FUSE-Net，这是一种新颖的深度学习模型，它结合了多尺度卷积层和基于MLP-Mixer的通道学习，以有效地建模空间和光谱依赖性。为了进行评估，我们通过在受控条件下拍摄罗勒叶构建了一个高分辨率RGB-HSI配对数据集。通过消融研究和波段组合分析，我们评估了模型恢复光谱信息的能力。实验结果表明，在包括均方误差（MSE）、峰值信噪比（PSNR）、光谱相关系数（SCC）和结构相似性（SSIM）在内的多个指标上，G-RGB输入始终优于未修改的RGB，与FUSE-Net配对时性能最佳。虽然我们的方法不能替代真实的近红外数据，但在只有RGB图像可用的推理过程中，它提供了一种可行的近似方法，支持在无法使用HSI系统的场景中进行经济高效的分析。
