Enhanced hyperspectral imaging for urban reconnaissance

Abstract

Combining images from airborne sensors enables accurate classification of materials and infrastructure in urban areas, enabling defense applications.A European Defence Agency framework has combined highspatial and -spectral resolution images from airborne sensors to improve detection and classification of objects in towns and cities. The project offers possible solutions to challenges in obtaining accurate georeferences, and explores the use of change detection algorithms and spectral matching to detect anomalies in the urban environment.The four-year framework (Detection in Urban scenario using Combined Airborne imaging Sensors, or DUCAS) combined the resources of Sweden, . We undertook an extensive field trial in Zeebrugge, Belgium in 2011, supplying instrumentation for 3D mapping, hyperspectral and high-resolution imagery, together with in situ techniques to measure target, background, and atmospheric characterizations. Analyzing data from the trial, we considered many different applications for remote sensing data, reporting preliminary results in 2012. 1 We checked the quality of the remotely sensed hyperspectral data using ground truth measurements (information collected at the site) and modeling of radiative transfer (the movement of radiation with regard to emission, absorption, and scattering). We compared measured spectra extracted from a hyperspectral image with simulated spectra from ground and atmospheric measurements and the MODTRAN computer program (MODerate resolution atmospheric TRANsmission). The measurements included temperature and humidity profiles, target spectral reflectance, surface temperature, and sensor characteristics.Due to the complexity of urban environments and human activities, the spatial and spectral characteristics of targets and backgrounds are extremely diverse. This makes the detection