Hyperspektralbildgebung

Hyperspectral imaging is a technique for acquiring and processing information from across the electromagnetic spectrum. Unlike conventional color images that use a few broad bands, hyperspectral systems collect data in tens to hundreds of narrow, contiguous spectral bands. Each pixel in a hyperspectral image contains a detailed spectral signature, enabling material identification and discrimination based on spectral properties. The set of individual narrow bands is often referred to as the hyperspectral spectrum or hyperspectral bands; together they form a data cube with two spatial dimensions and one spectral dimension.

Sensors used for hyperspectral imaging are deployed on airborne platforms and satellites, allowing coverage at varying altitudes and scales. Notable instruments include AVIRIS, Hyperion, HyMap, EnMAP, and PRISMA. Data cubes typically provide spatial resolution from meters to tens of meters per pixel and spectral coverage that ranges from visible to shortwave infrared, depending on the sensor design. Preprocessing steps include radiometric calibration, atmospheric correction to convert radiance to reflectance, and geometric correction, along with noise reduction. Dimensionality reduction methods such as principal component analysis or minimum noise fraction are commonly used to reduce data volume while preserving informative variation.

Applications of hyperspectral imaging span several fields. In agriculture and forestry, it supports crop health assessment and species discrimination. In geology and mineral exploration, it aids mineral mapping and rock identification. Environmental monitoring uses it for water quality and pollution detection, while urban planning and cultural heritage studies apply it for material characterization. In defense and remote sensing, hyperspectral data enable target detection and material identification beyond conventional imagery.

Limitations include large data volumes, computational demands, and sensitivity to atmospheric and lighting conditions. Accurate analysis often requires robust calibration, atmospheric correction, and high-quality spectral libraries.