U.S. Naval Research Lab using AI & Hyperspectral Imaging to Enhance Stealth of Warship

The U.S. Naval Research Laboratory (NRL) is developing artificial intelligence applications in hyperspectral imaging to enhance the stealth coat of warships to avoid detection by advanced (enemy) surveillance systems.

The technology "significantly improves our ability to detect and identify objects in crowded littoral zones," NRL researcher Katrina Doctor said, adding that improved hyperspectral detection is key to assessing threats and ensuring the (U.S) Navy maintains a clear operational advantage in any coastal environment.

Hyperspectral imaging, often described as capturing "the color of color," provides a unique spectral fingerprint for each pixel. Combined with AI, these fingerprints support powerful tools for detecting subtle material differences and observing environmental change.

The NRL is leveraging multiscale data collected from airborne platforms, unmanned aerial vehicles and satellites over engineered and natural targets at the Tait Preserve in Penfield, New York — an environment chosen for its coastal and aquatic-adjacent features.

Researchers collected measurements from custom-fabricated metal panels with painted coatings, as well as from natural rock and mineral samples. These targets serve as known reference points — like the bull's-eye on a target — for improving AI algorithms that solve a longstanding remote sensing challenge called hyperspectral unmixing.

Hyperspectral unmixing is the process of separating mixed spectral signatures within a single pixel. When unresolved, mixed pixels can obscure object detection and reduce identification accuracy — especially in complex, cluttered coastal environments.

Researchers collected measurements from custom-fabricated metal panels with painted coatings, as well as from natural rock and mineral samples. These targets serve as known reference points — like the bull's-eye on a target — for improving AI algorithms that solve a longstanding remote sensing challenge called hyperspectral unmixing.

The resulting AI systems will be better able to distinguish between natural environments, like the ocean surface, and coated, fabricated objects, like a ship's hull. The approach provides reference points for evaluating how AI-based unmixing performs across varying environmental and spectral conditions.