Achieving undetectable camouflage necessitates effectiveness across a broad wavelength spectrum depending on the opposing party's sensor sensitivity. While adapting the camouflage for the visible spectrum suffices for concealment from human sight, specialized sensors (e.g., night vision goggles and ultraviolet (UV) cameras) demand a broader spectral coverage. By using materials not occurring in the environment can result in apparent distinctions in spectral signatures. Challenges arise due to water absorption in the near- (NIR) and shortwave infrared (SWIR) regions, making camouflage noticeable amidst water-containing foliage. Discrepancies in light polarization between artificial and natural structures also contribute to contrast. Detection of these spectral variations is facilitated by multispectral or hyperspectral cameras in the IR range, but cost-effective alternatives like night vision goggles and broadband cameras are more commonly employed. We propose a cost-effective approach to enhance lower-tier equipment by identifying spectral regions with higher contrast between the target object and its background. To take advantage of the identified regions, appropriate optical filters or polarizers will be attached to sensors, effectively amplifying contrast and improving detectability, with applications in surveillance and national security. A comprehensive system will be designed to measure diffuse reflectance in the UV + VIS + NIR + SWIR regions (200–2500 nm). A standardized protocol for measuring diffuse reflectance will ensure measurement consistency and comparability. The methodology will involve capturing broadband photographs across various spectral regions, utilizing an integrating sphere for point reflectance spectra measurements, and assembling hyperspectral imaging systems using imaging spectrographs in the NIR and SWIR regions. The library generated from these measurements will compile reflectance spectra of diverse environmental features, including leaves in different hydration states, bark, representative rock types, and other backgrounds, as well as common and camouflaged textiles in different states (wet, aged, stained, ...). Polarization changes due to interactions with the environment or camouflaged objects will also be explored. A quantitative metric will be formulated to assess the contrast between camouflaged fabric and environmental spectra, employing statistical and potentially deep learning methods. Automated strategies will be explored for comprehensive analysis and identification of spectral regions of increased contrast. Simulations based on transmittance data will be performed to evaluate the utility of selected filters, considering factors such as center wavelength, full width at half maximum (FWHM), and cost. The proposed enhancements' effectiveness in camouflage detection will be evaluated through standard assessment techniques, including subjective assessments with human observers and real-time camera-based evaluations in various scenarios (e.g., wet terrain, heavy cloud coverage, low light conditions, ...).
