## 2 Related Work  
### Multi-Spectral Imaging  
Multi-spectral imaging, with its ability to capture chemical and physical properties through distinct wavelength bands, has emerged as a transformative technology across diverse domains. Its versatility has been demonstrated in areas ranging from agriculture and heritage conservation to satellite imaging and marine ecosystem monitoring.  
In agricultural research, this technology has proven invaluable for addressing challenges in resource management and crop production. For instance, [PERSON] et al. [6] utilized near-infrared (NIR) spectroscopy to analyze nutrient levels in plant leaf tissues, presenting a rapid and cost-efficient alternative to conventional nutrient assessment methods. Expanding on this, [PERSON] et al. [7] investigated the potential of SWIR bands to detect crop residues and evaluate soil health, enabling more precise soil management strategies. Additionally, [PERSON] et al. [8] developed a system to identify haploid seeds using spectral cameras operating at 980 nm and 1050 nm, leveraging the absorption properties of these wavelengths to boost corn yield.  
The scope of multi-spectral imaging extends beyond land-based applications to environmental monitoring. In marine ecosystems, [PERSON] et al. [9] applied a 10-band multi-spectral camera mounted on a UAV to monitor ecological dynamics. By employing advanced classification algorithms--such as Maximum Likelihood Classifier (MLC), Minimum Distance Classifier (MDC), and Spectral Angle Classifier (SAC)--their approach facilitated detailed ecosystem mapping and monitoring.  
Heritage conservation has also greatly benefited from the non-invasive capabilities of multi-spectral imaging. [PERSON] et al. [10] utilized a combination of visible and infrared spectroscopy, including NIR and SWIR, to examine the composition of artworks and refine reflectography methods for enhanced imaging detail. [PERSON] et al. [11] expanded on these techniques by characterizing the composition and preservation state of Xuan paper through NIR spectroscopy. Similarly, [PERSON] [12] showcased the effectiveness of NIR and SWIR wavelengths in uncovering hidden attributes of archaeological and artistic artifacts, driving significant progress in artifact conservation methodologies.  
Satellite imaging has also seen significant improvements through multi-spectral techniques. [PERSON] et al. [13] utilized NIR and SWIR bands for the vicarious calibration of satellite ocean color sensors, enhancing data accuracy. [PERSON] and [PERSON] [14] leveraged sharpened VNIR (visible and NIR) and SWIR bands from WorldView-3 imagery for mineral detection, demonstrating superior spatial resolution. [PERSON] et al. [15] used SWIR imaging to improve object detection in challenging conditions such as fog, outperforming traditional RGB-based methods.  
As the focus shifts to autonomous driving, multi-spectral imaging becomes increasingly important for addressing perception challenges under adverse environmental conditions. [PERSON] et al. [16] highlighted the extensive use of SWIR, NIR, and long-wave infrared (LWIR) to enhance vehicle perception across various weather scenarios to ensure reliable operation in daytime and nighttime environments. [PERSON] et al. [17] used LWIR imaging for vehicle detection and tracking in conditions of low visibility, such as fog and rain. Similarly, [PERSON] et al. [18] demonstrated the effective use of NIR, SWIR, and LWIR for autonomous navigation during severe winter conditions, showcasing their resilience in harsh environments. Building on this, [PERSON] et al. [19] explored the integration of polarized LWIR sensors with convolutional neural networks to enhance situational awareness in varying weather conditions. [PERSON] et al. [20] conducted a comprehensive evaluation of LWIR, SWIR, visible wavelengths, and LiDAR performance under foggy conditions. Their findings highlighted the strengths and limitations of these spectral bands in maintaining perception and safety in autonomous systems.